Browse publications by year: 2024

  1. Isayama T, Miyake F, Rohsiswatmo R, Dewi R, Ozawa Y, Tomotaki S, et al.
    BMJ Open, 2024 Jul 13;14(7):e082712.
    PMID: 39388526 DOI: 10.1136/bmjopen-2023-082712
    INTRODUCTION: Reducing neonatal deaths in premature infants in low- and middle-income countries is key to reducing global neonatal mortality. International neonatal networks, along with patient registries of premature infants, have contributed to improving the quality of neonatal care; however, the involvement of low-to-middle-income countries was limited. This project aims to form an international collaboration among neonatal networks in Asia (AsianNeo), including low-, middle- and high-income countries (or regions). Specifically, it aims to determine outcomes in sick newborn infants, especially very low birth weight (VLBW) infants or very preterm infants, with a view to improving the quality of care for such infants.

    METHODS AND ANALYSIS: Currently, AsianNeo comprises nine neonatal networks from Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Sri Lanka, Taiwan and Thailand. AsianNeo will undertake the following four studies: (1) institutional questionnaire surveys investigating neonatal intensive care unit resources and the clinical management of sick newborn infants, with a focus on VLBW infants (nine countries/regions); (2) a retrospective cohort study to describe and compare the outcomes of VLBW infants among Asian countries and regions (four countries/regions); (3) a prospective cohort study to develop the AsianNeo registry of VLBW infants (six countries/regions); and (4) implementation and evaluation of educational and quality improvement projects in AsianNeo countries and regions (nine countries/regions).

    ETHICS AND DISSEMINATION: The study protocol was approved by the Research Ethics Board of the National Center for Child Health and Development, Tokyo, Japan (reference number 2020-244, 2022-156). The study findings will be disseminated through educational programmes, quality improvement activities, conference presentations and medical journal publications.

    MeSH terms: Asia; Humans; Infant; Infant Mortality; Infant, Newborn; Infant, Premature; Intensive Care Units, Neonatal/organization & administration; Intensive Care Units, Neonatal/standards; International Cooperation; Quality of Health Care; Surveys and Questionnaires; Research Design; Retrospective Studies; Infant, Very Low Birth Weight
  2. Saeidi H, Sarafbidabad M
    Mol Biol Rep, 2024 Oct 30;51(1):1103.
    PMID: 39476131 DOI: 10.1007/s11033-024-10034-5
    Despite recent advancements in the treatment of metastatic castrate-resistant prostate cancer (mCRPC), this disease remains lethal. A novel family of targeted pharmaceuticals known as poly-ADP-ribose polymerase (PARP) inhibitors has been developed to treat mCRPC patients with homologous recombination repair (HRR) gene alterations. The FDA recently approved olaparib and rucaparib for treating mCRPC patients with HRR gene alterations. Ongoing trials are investigating combination therapies involving PARP inhibitors combined with radiation, chemotherapy, immunotherapy, and androgen receptor signaling inhibitors (ARSIs) to improve the effectiveness of PARP inhibitors and broaden the range of patients who can benefit from the treatment. This review provides an overview of the development of PARP inhibitors in prostate cancer and analyzes the mechanisms underlying their resistance.
    MeSH terms: Antineoplastic Agents/pharmacology; Antineoplastic Agents/therapeutic use; Humans; Indoles/pharmacology; Indoles/therapeutic use; Male; Phthalazines/pharmacology; Phthalazines/therapeutic use; Piperazines/pharmacology; Piperazines/therapeutic use; Prostatic Neoplasms/drug therapy; Prostatic Neoplasms/genetics; Drug Resistance, Neoplasm/genetics; Recombinational DNA Repair/drug effects
  3. Ong SM, McKenna C, Pinard C, Richardson D, Oblak ML
    Front Vet Sci, 2024;11:1519636.
    PMID: 39906044 DOI: 10.3389/fvets.2024.1519636
    OBJECTIVES: To evaluate the prognostic factors and treatment outcomes in dogs with high-grade cutaneous mast cell tumors (HGMCTs).

    METHODS: Medical records of dogs with a histopathologic diagnosis of HGMCTs were reviewed from a single institution. Clinical factors, treatment-related variables, and adjuvant therapies were documented to evaluate their association with clinical outcomes. Comparative and survival analyses were conducted using Kaplan-Meier survival analysis, log-rank, and Fisher's exact tests.

    RESULTS: The overall median survival time for the 77 dogs was 317 days (range 20-3,041 days) with 6-month, 1-year, and 2-year survival rates of 69, 50, and 30%, respectively. Surgically treated dogs had significantly prolonged survival and were 6.88 times more likely to survive beyond 5.5 months. The presence of metastasis at initial staging was strongly associated with poorer outcomes, as dogs without metastasis at initial staging had 6.94 times higher odds of surviving beyond 2 years. Surgical sites with incomplete margins had a higher local recurrence rate (58%) compared to those with clean margins (26%). Despite aggressive treatment, 75% of the dogs that received concurrent surgical and adjuvant therapy experienced disease progression. Lymph node extirpation, tumor localization, number of tumors, and local recurrence were not associated with the overall outcome.

    CLINICAL RELEVANCE: The combination of aggressive local therapy and adjuvant systemic chemotherapy provides a notable survival benefit in dogs with HGMCTs. The limited therapeutic benefit of locoregional lymph node extirpation, combined with a persistently high metastatic rate despite systemic chemotherapy, highlights the critical need for more effective regional and systemic treatment approaches for HGMCT patients.

  4. Kuruppu M, Siddiqui Y, Khalil HB
    Front Microbiol, 2024;15:1514235.
    PMID: 39906538 DOI: 10.3389/fmicb.2024.1514235
    Malaysia ranks among the world's top 20 pineapple producers, driven by the success of the MD2 variety in meeting domestic and international demand. However, postharvest losses due to pathological diseases remain a challenge. Black rot, a major postharvest disease, causes significant economic losses in pineapples. Despite its presence in various cultivars, its aetiology, specifically in MD2 pineapples remains unclear. This study was conducted to identify the principal causative pathogen of black rot disease in pineapple from three different regions. In addition, critical factors influencing black rot disease were investigated, such as the minimum inoculum concentration, appropriate storage temperature, and maturity index required to initiate infection. Thielaviopsis paradoxa was identified as the primary pathogen causing black rot, with 50 and 45% occurrence at two specific cultivation sites. Other associated pathogens included Lasiodiplodea theobromae, Trichoderma asperellum, Curvularia eragrostidis, Neoscytalidium dimidiatum, Aspergillus assiutensis, and Aspergillus aculeatus. Fruits stored at ambient temperature with a maturity index of 2 showed higher disease progression than those in cold storage. A minimum inoculum concentration of 1 × 104 CFU/mL was sufficient for infection at both storage conditions. The Pearson correlation analysis revealed a weak positive link (r > 0.39, p 
  5. Medeleanu MV, Reyna ME, Dai DLY, Winsor GL, Brinkman FSL, Verma R, et al.
    Front Allergy, 2024;5:1463867.
    PMID: 39906720 DOI: 10.3389/falgy.2024.1463867
    OBJECTIVE: Lower respiratory tract infections (LRTIs) in early life are one of the strongest risk factors for childhood asthma and are often treated with systemic antibiotics (IV or oral). We aimed to explore the association between early-life LRTIs and systemic antibiotics on asthma development and the potential mediating role of antibiotics in this relationship.

    METHODS: Data were collected as part of the longitudinal, general Canadian population CHILD Study. LRTIs during the first 18 months of life were identified through parental symptom report at regular study visits. Systemic antibiotic use was defined as at least one dose of oral/intravenous antibiotics between birth and the 18-month visit and were further categorized by indication as either given for a respiratory indication (upper or lower respiratory symptoms) or non-respiratory indication. Asthma was diagnosed by in-study pediatricians at the 5-year study visit. Adjusted logistic regression models and mediation analyses via systemic antibiotics use were performed.

    RESULTS: Among 2,073 participants included in our analysis, 72 (4.9%) had asthma age 5, and 609 (29.3%) used systemic antibiotics before the 18-month visit. Among children who had taken antibiotics, 61.6% also had an LRTI in that period compared to 49.7% among children without exposure to systemic antibiotics (p 

  6. Sarskov SA, Vyushkov MV, Slavin SL, Zaitseva NN
    Sovrem Tekhnologii Med, 2024;16(6):17-22.
    PMID: 39896150 DOI: 10.17691/stm2024.16.6.02
    The aim of the study is to develop additional analytical modules of geoinformation software complex on current infectious and parasitic diseases aimed to improve the quality of epidemiological monitoring and to generate a database on the trends of epidemical process development in the subjects of the Russian Federation.

    MATERIALS AND METHODS: Additional analytical blocks on comparative and dynamic analysis of morbidity by the groups of nosologies were developed using the software meeting the general concept of the software complex (JavaScript, PHP, and others) and integrated into a new version of the Web application "Epidemiological Atlas of Russia. Territory of the Federal District". The initial data including information by the groups of diseases were converted into a set of interrelated tables with their further integration into the database of a new version of the Atlas under the control of a free relational MySQL database management system.

    RESULTS: The existing classifications of nosologic forms and the search for additional characteristics, potentially forming the groups of nosologies, have been analyzed and the current database of the Epidemiological Atlas has been optimized. The algorithms for obtaining and evaluating epidemiological indicators in the new analytical blocks for estimating cumulative morbidity by the nosologic groups were designed. There were created original analytical modules "Comparative analysis of morbidity by the groups of nosologies" and "Dynamic analysis of morbidity by the groups of nosologies" for the Web application "Epidemiological Atlas of Russia. Territory of Federal District" for the comparative and dynamic morbidity analysis based on the groups of nosologies in the administrative-territorial subject units, in the district subjects, and in the district as a whole, with the possibility of information detailing. The materials based on the database queries contain temporal (calendar month) and spatial detailing (administrative-territory unit of the Russian Federation subject). All materials may be exported as tables, graphs, or maps in various formats (.xls, .pdf, .csv, .png, .jpeg, .svg). Since the databases of the current epidemiological atlases of the Volga Federal District and Russia are universal, the mechanisms of processing tables and queries are identical providing the possibility of using the developed approaches employed in the Epidemiological Atlas of Russia or atlases of other federal districts in case of replicating a new Web application version. New analytical blocks may extend notions on the incidence of current infectious diseases and reveal characteristic regional features, facilitate more exact scientifically grounded proposals for decision-making by the executive authorities and timely taking preventive and anti-epidemic measures.

    CONCLUSION: The developed analytical modules integrated into the new version of the "Epidemiological Atlas of Russia, Territory of the Federal District" were designed to extend the analytical capabilities of the geoinformation software complex. They are characterized by a high significance in optimization and quality improvement of epidemiological monitoring, operative and retrospective epidemiological analysis of current infectious and parasitic diseases in a separate subject, a federal district, and the Russian Federation as a whole, and represent an essential potential for further improvement of analytical methods and technologies.

    MeSH terms: Humans; Russia/epidemiology; Software*; Databases, Factual; Geographic Information Systems
  7. Wong JJM, Dang H, Gan CS, Phan PH, Kurosawa H, Aoki K, et al.
    Crit Care Med, 2024 Oct 01;52(10):1602-1611.
    PMID: 38920618 DOI: 10.1097/CCM.0000000000006357
    OBJECTIVES: Despite the recommendation for lung-protective mechanical ventilation (LPMV) in pediatric acute respiratory distress syndrome (PARDS), there is a lack of robust supporting data and variable adherence in clinical practice. This study evaluates the impact of an LPMV protocol vs. standard care and adherence to LPMV elements on mortality. We hypothesized that LPMV strategies deployed as a pragmatic protocol reduces mortality in PARDS.

    DESIGN: Multicenter prospective before-and-after comparison design study.

    SETTING: Twenty-one PICUs.

    PATIENTS: Patients fulfilled the Pediatric Acute Lung Injury Consensus Conference 2015 definition of PARDS and were on invasive mechanical ventilation.

    INTERVENTIONS: The LPMV protocol included a limit on peak inspiratory pressure (PIP), delta/driving pressure (DP), tidal volume, positive end-expiratory pressure (PEEP) to F io2 combinations of the low PEEP acute respiratory distress syndrome network table, permissive hypercarbia, and conservative oxygen targets.

    MEASUREMENTS AND MAIN RESULTS: There were 285 of 693 (41·1%) and 408 of 693 (58·9%) patients treated with and without the LPMV protocol, respectively. Median age and oxygenation index was 1.5 years (0.4-5.3 yr) and 10.9 years (7.0-18.6 yr), respectively. There was no difference in 60-day mortality between LPMV and non-LPMV protocol groups (65/285 [22.8%] vs. 115/406 [28.3%]; p = 0.104). However, total adherence score did improve in the LPMV compared to non-LPMV group (57.1 [40.0-66.7] vs. 47.6 [31.0-58.3]; p < 0·001). After adjusting for confounders, adherence to LPMV strategies (adjusted hazard ratio, 0.98; 95% CI, 0.97-0.99; p = 0.004) but not the LPMV protocol itself was associated with a reduced risk of 60-day mortality. Adherence to PIP, DP, and PEEP/F io2 combinations were associated with reduced mortality.

    CONCLUSIONS: Adherence to LPMV elements over the first week of PARDS was associated with reduced mortality. Future work is needed to improve implementation of LPMV in order to improve adherence.

    MeSH terms: Adolescent; Child; Child, Preschool; Female; Humans; Infant; Male; Positive-Pressure Respiration/methods; Prospective Studies; Tidal Volume; Intensive Care Units, Pediatric; Controlled Before-After Studies
  8. Samat AHA, Cassar MP, Akhtar AM, McCracken C, Ashkir ZM, Mills R, et al.
    Int J Cardiol, 2024 Nov 15;415:132415.
    PMID: 39127146 DOI: 10.1016/j.ijcard.2024.132415
    BACKGROUND: The role of ECG in ruling out myocardial complications on cardiac magnetic resonance (CMR) is unclear. We examined the clinical utility of ECG in screening for cardiac abnormalities on CMR among post-hospitalised COVID-19 patients.

    METHODS: Post-hospitalised patients (n = 212) and age, sex and comorbidity-matched controls (n = 38) underwent CMR and 12‑lead ECG in a prospective multicenter follow-up study. Participants were screened for routinely reported ECG abnormalities, including arrhythmia, conduction and R wave abnormalities and ST-T changes (excluding repolarisation intervals). Quantitative repolarisation analyses included corrected QT (QTc), corrected QT dispersion (QTc disp), corrected JT (JTc) and corrected T peak-end (cTPe) intervals.

    RESULTS: At a median of 5.6 months, patients had a higher burden of ECG abnormalities (72.2% vs controls 42.1%, p = 0.001) and lower LVEF but a comparable cumulative burden of CMR abnormalities than controls. Patients with CMR abnormalities had more ECG abnormalities and longer repolarisation intervals than those with normal CMR and controls (82% vs 69% vs 42%, p 

    MeSH terms: Adult; Aged; Female; Follow-Up Studies; Humans; Male; Middle Aged; Prospective Studies
  9. Al-Rawi MBA, Khan AHI, Sheikh Ghadzi SM
    Risk Manag Healthc Policy, 2024;17:3327-3339.
    PMID: 39742076 DOI: 10.2147/RMHP.S456155
    BACKGROUND AND AIMS: Chronic disease is a lifelong disorder that necessitates continuing medical care and is more prone to infections such as COVID-19, compared to healthy individuals. Therefore, this study aimed to assess the severity of COVID-19 among chronic disease patients in the Kingdom of Saudi Arabia.

    METHODS AND MATERIALS: A cross-sectional study was conducted in selected hospitals in the Riyadh region in Saudi Arabia, over 6 months in 2022. All participants' records were reviewed for socio-demographic data including age, gender, residence, marital status, level of education, occupation, and special habits such as smoking or addiction. In addition to this main complaint and present history, history of chronic illnesses, drug intake, surgical interference, general examination findings including vital signs, state of consciousness, general condition at admission and discharge, and outcome of cases were recorded.

    RESULTS: The mean age of the patient was 54.46 ± 15.85 (median of 53.67 years). In this study, the severity of COVID-19 was significantly associated with chronic diseases. For instance, 22.31% of the patients with diabetes reported mild symptoms, compared with 77.69% of the patients without diabetes. The current findings reported 2.18% of COVID-19 patients with respiratory diseases and 97.82% of the patients without respiratory diseases reported mild symptoms of COVID-19 infection. In comparison, 97.75% of COVID-19 patients without respiratory diseases and 2.25% of patients with respiratory diseases reported moderately severe COVID-19 infection.

    CONCLUSION: The current findings revealed that 66.2% of the COVID-19 patients with chronic diseases were free of symptoms, 5.3% of them died and 0.9% of the patients were in a worse situation. The severity of COVID-19 was significantly associated with the presence of chronic diseases. Additionally, medical practitioners must be more knowledgeable about the long-term illnesses that put patients at risk for serious COVID-19 challenges and mortality.

  10. Veiskarami A, Malekie S, Kashian S, Tajudin SM
    Rep Pract Oncol Radiother, 2024;29(4):413-425.
    PMID: 39895958 DOI: 10.5603/rpor.101397
    BACKGROUND: Polymer-carbon nanostructures have previously been introduced for dosimetry of gamma rays with potential application in radiotherapy. In this research work, bismuth oxide (Bi2O3) nanoparticles were added into the amorphous polycarbonate (PC) matrix to enhance the probability of the photoelectric effect and dosimetry response in parallel.

    MATERIALS AND METHODS: PC/Bi2O3 nanocomposites at concentrations of 0, 5, 20, 40, and 50 Bi2O3 wt% were fabricated via a solution method. Afterward, the samples were irradiated by gamma rays of cobalt-60 (60Co) related to Picker V-9, and Therarton-780 machines at 30-254 mGy/min. Dosimetric characteristics were carried out including linearity, angular dependency, energy, bias-polarity, field size, and repeatability.

    RESULTS: Field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) analyses exhibited an appropriate dispersion state. The dosimeter response was linear at 30-254 mGy/min for the all samples. The 50 wt% sample exhibited the highest sensitivity at 4.61 nC/mGy. A maximum angular variation of approximately 15% was recorded in normal beam incidence. The energy dependence at two energies of 662 and 1250 keV was obtained as 0.7%. Bias-polarity for the 40, and 50 wt% samples at 400 V were measured as 15.9% and 9.0%, respectively. The dosimetry response was significantly dependent on the radiation field size. Also, the repeatability of the dosimeter response was measured as 0.4%.

    CONCLUSIONS: Considering the dosimetry characteristics of PC-Bi2O3 nanocomposites, and appropriate correction factors, this material can be used as a real-time dosimeter for the photon fields at therapy level.

  11. Abdul Razak SF, Azman M, Ping LS
    Oman Med J, 2024 Jul;39(4):e656.
    PMID: 39896116 DOI: 10.5001/omj.2024.23
    The superior cornu of the thyroid cartilage is a versatile structure, and anatomical variations can lead to diverse clinical presentations. We describe a case of a patient with a medialized superior part of the thyroid cartilage caused by pressure from a large thyroid mass, detected during laryngoscopy before thyroidectomy. A neck computed tomography scan revealed an elongated and medially displaced superior cornu of the right thyroid cartilage, resulting from the push exerted by the right thyroid mass. As the patient remained asymptomatic and refused surgical intervention, no further consideration was given to surgically addressing the medialized superior thyroid cornu.
  12. Xiao J, Abidin SZ, Vermol VV, Gong B
    PeerJ Comput Sci, 2024;10:e2442.
    PMID: 39896361 DOI: 10.7717/peerj-cs.2442
    The global tourism industry is expanding rapidly, making effective management of hotel booking cancellations crucial for improving service and efficiency. Existing models, based on static data assumptions and fixed parameters, fail to capture dynamic changes and temporal trends. Real-world cancellation decisions are influenced by factors such as seasonal variations, market demand fluctuations, holidays, and special events, which cause significant changes in cancellation rates. Traditional models struggle to adjust dynamically to these changes. This article proposes a novel approach using deep reinforcement learning techniques for predicting hotel booking cancellations over time. We introduce a framework that combines dynamic temporal reinforcement learning with policy-enhanced LSTM, capturing temporal dynamics and leveraging multi-source information to improve prediction accuracy and stability. Our results show that the proposed model significantly outperforms traditional methods, achieving over 95.9% prediction accuracy, a model stability of 0.98, an F1 Score approaching 1, and a mutual information score of approximately 0.93. These results validate the model's effectiveness and generalization across diverse data sources. This study provides an innovative and efficient solution for managing hotel booking cancellations, demonstrating the potential of deep reinforcement learning in handling complex prediction tasks.
  13. Niu J, Shen C, Zhang L, Li Q, Ma H
    PeerJ Comput Sci, 2024;10:e2620.
    PMID: 39896365 DOI: 10.7717/peerj-cs.2620
    BACKGROUND: The widespread adoption of plant protection robots has brought intelligent technology and agricultural machinery into deep integration. However, with advances in robotic autonomy, the energy that robots can carry remains limited due to constraints on battery capacity and weight. This limitation restricts the robots' ability to perform tasks continuously over extended periods.

    METHODS: To address the challenges of achieving low energy consumption and efficiency in path planning for plant protection robots operating in mountainous environments, a multi-objective path optimization approach was developed. This approach combines the improved A* algorithm with the Improved Whale Optimization Algorithm (A*-IWOA), utilizing a 2.5D elevation grid map. First, an energy consumption model was created to account for the robot's energy use on slopes, based on its kinematic and dynamic models. Then, an improved A* search method was established by expanding to an 8-domain diagonal distance search and introducing a cost function influenced by cross-product decision values. Using the robot's motion trajectory as a constraint, the IWOA algorithm was applied to optimize the vector cross-product factor (p) by dynamically adjusting population positions and inertia weights, to minimize both energy consumption and path curvature. Finally, in simulation and orchard scenarios, the application effects of the proposed algorithm were evaluated and compared against notable variants of the A* algorithm using the robot ROS 2 operating system.

    RESULTS: The experimental results show that the proposed algorithm substantially reduces the travel distance and enhances both path planning and computational efficiency. The improved approach meets the driving accuracy and energy consumption requirements for plant protection robots operating in mountainous environments.

    DISCUSSION: This algorithm offers significant advantages in terms of computational accuracy, convergence speed, and efficiency. Moreover, the resulting paths satisfy the stringent energy consumption and path planning requirements of robots in unstructured mountain terrain. This improved algorithm could also be replicated and applied to other fields, such as picking robots, factory inspection robots, and complex industrial environments, where robust and efficient path planning is required.

  14. Islam MS, Al Farid F, Shamrat FMJM, Islam MN, Rashid M, Bari BS, et al.
    PeerJ Comput Sci, 2024;10:e2517.
    PMID: 39896401 DOI: 10.7717/peerj-cs.2517
    The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.
  15. Hajim WI, Zainudin S, Daud KM, Alheeti K
    PeerJ Comput Sci, 2024;10:e2520.
    PMID: 39896419 DOI: 10.7717/peerj-cs.2520
    Advanced machine learning (ML) and deep learning (DL) methods have recently been utilized in Drug Response Prediction (DRP), and these models use the details from genomic profiles, such as extensive drug screening data and cell line data, to predict the response of drugs. Comparatively, the DL-based prediction approaches provided better learning of such features. However, prior knowledge, like pathway data, is sometimes discarded as irrelevant since the drug response datasets are multidimensional and noisy. Optimized feature learning and extraction processes are suggested to handle this problem. First, the noise and class imbalance problems must be tackled to avoid low identification accuracy, long prediction times, and poor applicability. This article aims to apply the Non-Negativity-Constrained Auto Encoder (NNCAE) network to tackle these issues, enhance the adaptive search for the optimal size of sliding windows, and ensure that deep network architectures are adept at learning the vital hidden features. NNCAE methodology is used after performing the standard pre-processing procedures to handle the noise and class imbalance problem. This class balanced and noise-removed input data features are learned to train the proposed hybrid classifier. The classification model, Golden Eagle Optimization-based Convolutional Long Short-Term Memory neural networks (GEO-Conv-LSTM), is assembled by integrating Convolutional Neural Network CNN and LSTM models, with parameter tuning performed by the GEO algorithm. Evaluations are conducted on two large datasets from the Genomics of Drug Sensitivity in Cancer (GDSC) repository, and the proposed NNCAE-GEO-Conv-LSTM-based approach has achieved 96.99% and 97.79% accuracies, respectively, with reduced processing time and error rate for the DRP problem.
  16. Bhardwaj A, Bhardwaj A, Bhumika, Sirowa A
    Ind Psychiatry J, 2024;33(2):409-413.
    PMID: 39898083 DOI: 10.4103/ipj.ipj_134_24
    Psychotropic medications, particularly antipsychotics, are known to elicit various adverse effects, with ocular complications being underreported yet significant. This case series presents three instances where atypical antipsychotics, namely Risperidone, Cariprazine, and Olanzapine, led to ocular dystonias and nystagmus. These adverse effects occurred at relatively low doses, highlighting the need for vigilant monitoring even with second-generation antipsychotics. Case descriptions delineate patients experiencing acute dystonic reactions and nystagmus following initiation or dose adjustment of atypical antipsychotics, leading to upward deviation of the eyes, involuntary movements, and nystagmus. Prompt recognition and management were crucial, with cessation of the offending medication resulting in symptom remission and subsequent stabilization with alternative treatments. Factors contributing to these adverse effects, such as dopamine receptor blockade and individual susceptibility, are explored, emphasizing the importance of comprehensive evaluation and open patient-physician communication. The present case series underscores the necessity of vigilant monitoring for ocular adverse effects, even with atypical antipsychotics, given their potential to induce acute dystonias and nystagmus. The presented cases advocate for heightened awareness among clinicians to promptly recognize and manage such rare complications, ensuring optimal patient care and treatment outcomes.
  17. Joshi A, Pradhan B, Chakraborty S, Varatharajoo R, Alamri A, Gite S, et al.
    Front Plant Sci, 2024;15:1491493.
    PMID: 39898259 DOI: 10.3389/fpls.2024.1491493
    Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored. Interpretability is another pressing and challenging issue in DL-based crop yield prediction, a factor that ensures the reliability of the model. Thus, this study aims to develop and implement an explainable DL model capable of accurately predicting crop yield and providing explanations for the predictions. To achieve this, we developed three state-of-the-art sequential DL models: LSTM, 1D CNN, and Bi-LSTM. We then employed three popular interpretability techniques: Local interpretable model-agnostic explanations (LIME), Integrated Gradient (IG) and Shapley Additive Explanation (SHAP) to understand the decision-making process of the models. The Bi-LSTM model outperformed other models in terms of predictive performance (R2 up to 0.88) and generalizability across locations and ranges of yield data. Explainability analysis reveals that enhanced vegetation index (EVI), temperature and precipitation at later stages of crop growth are most important in determining Winter wheat yield. Further, we demonstrated that XAI methods can also be used to understand the decision-making process of the models, to understand instances such as high- and low-yield samples, to find possible explanations for erroneous predictions, and to identify regions impacted by particular stress. By employing advanced DL techniques along with an innovative approach to explainability, this study achieves highly accurate yield prediction while providing intuitive insights into the model's decision-making process.
  18. Wei M, Yusuf A, Hsien CCM, Marzuki MA
    Int J Nurs Stud, 2024 Dec 18;164:104983.
    PMID: 39899940 DOI: 10.1016/j.ijnurstu.2024.104983
    BACKGROUND: Cancer is a life-threatening disease that can have a significant impact on patients' psychological well-being. Behavioural activation is an emerging psychological therapy that has been suggested effective in improving depression and anxiety. However, no review has yet summarised its effects on psychological distress among people with cancer.

    OBJECTIVE: To identify studies of behavioural activation designed for people with cancer and examine the effects on psychological distress, including depression and anxiety.

    DESIGN: Systematic review and meta-analysis.

    METHODS: A systematic search of PubMed/MEDLINE, CINAHL, EMBASE, PsycINFO, and the Cochrane Library was performed from the inception to 6 April 2024. Randomised controlled trials reporting on the effects of behavioural activation on psychological distress among cancer patients were included. Two authors independently screened the eligible studies, assessed the quality of studies, and extracted data. The risk of bias was assessed using version 2 of the Cochrane risk-of-bias tool for randomised trials (RoB 2). The meta-analysis was performed by Review Manager 5.4, and narrative synthesis was employed when the meta-analysis was inappropriate. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system was used to assess the certainty of the evidence.

    RESULTS: A total of nine studies were included in this systematic review, with 1811 participants. The pooled analysis showed that behavioural activation could improve depression (SMD = -0.24, 95 % CI -0.44 - -0.03, p = 0.020; moderate quality of evidence), and anxiety (SMD = -0.56, 95 % CI -1.01 - -0.10, p = 0.020; low quality of evidence) among people with cancer. The effects were robust in sensitivity analysis and yielded consistent results in studies that were not pooled due to insufficient data. Subgroup analyses suggested that face-to-face and group administration were more effective, whereas the effects of different dosages were uncertain. Besides, the effects of behavioural activation at different follow-up periods were not identified There was no consensus on the optimal components of intervention.

    CONCLUSIONS: The evidence for behavioural activation as an effective treatment of psychological distress among people with cancer is promising. However, it should be noted that the quality of evidence was moderate and low, thus emphasising the need for caution when applying these findings. In order to explore which components may be most effective in improving psychological outcomes, more rigorous study designs and more detailed descriptions of interventions are necessary.

    REGISTRATION: The protocol was registered on PROSPERO (Registration number: CRD42024533171).

  19. Long S, Madon ZB, Norowi NM, Ang MF
    Front Public Health, 2024;12:1431996.
    PMID: 39901910 DOI: 10.3389/fpubh.2024.1431996
    INTRODUCTION: The mental health of left-behind children has garnered attention from Chinese scholars in recent years. Although several interventions have been implemented to address these children's mental health in urban areas, a gap remains in understanding the types of interventions, their effectiveness, and the factors that act as barriers or facilitators during the implementation process.

    METHODS: A mixed methods systematic review informed by JBI methodology. Researchers conducted a comprehensive search of databases in both English and Chinese, covering the years 2005 to 2023. The initial search took place in January 2024 and was updated in March 2024. This study includes all studies results available up to December 31, 2023. The protocol has been registered on PROSPERO (CRD42023384078) and includes 14 studies in the review.

    RESULTS: The activity categories included group psychological activities, individual family activities and multiple formats services. Three barriers to implementation emerged: social workers, activities and parents. The facilitators were parents and activity design.

    CONCLUSION: This review revealed that some studies suffered from poor data collection methods and data quality. Studies on services for mental health in urban left-behind children requires methodologically robust study designs for broader dissemination and rigorous evaluation.

    SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42023384078, available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023384078.

    MeSH terms: Child; China; Humans; Mental Health; Mental Health Services; Transients and Migrants/psychology; Transients and Migrants/statistics & numerical data
  20. Gareeballah A, Gameraddin M, Alshoabi SA, Alsaedi A, Elzaki M, Alsharif W, et al.
    Front Oncol, 2024;14:1474930.
    PMID: 39902128 DOI: 10.3389/fonc.2024.1474930
    INTRODUCTION: Adnexal masses are a common health issue in gynecology; the challenge lies in the differential diagnosis of these masses. The International Ovarian Tumor Analysis Simple Rules (IOTA-SR) offers the first scoring system to aid in diagnosis. It is based on a set of five ultrasound imaging features indicative of a malignant ovarian tumor and five features indicative of a benign tumor. This review aims to assess the diagnostic performance of IOTA-SR for classifying ovarian tumors as benign or malignant.

    METHODS: A systematic review was conducted on MEDLINE, Embase, Google Scholar, Scopus, and Web of Science. The terminologies "IOTA-SR", "adnexal, mass", and "ovarian tumors scoring" were employed. Twenty-seven research articles conducted from 2008 to 2022 were included in the meta-analysis; the publication outcome indicates that performance quality tests were extracted directly or indirectly, including true positive (TP), false positive (FP), true negative (TN), and false negative (FN). The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the study quality and estimate the risk of bias. After estimating the pooled effect of the sensitivity, specificity, and diagnostic odds ratio (DOR), the summary receiver operating characteristic (SROC) curve was estimated using the bivariate random effects model. Utilizing Cochran's Q statistics and Higgins's inconsistency test through the I2 index for pooled analysis, the heterogeneity of studies was quantitatively evaluated. The funnel plot and Egger's test were utilized to visually and quantitatively evaluate potential publication bias.

    RESULTS: Among 27 studies, including 7,841 adnexal masses, the results of this meta-analysis showed excellent diagnostic performance with a pooled sensitivity of 92% [95% confidence interval (CI), 0.89-0.94] and a pooled specificity of 92% (95% CI, 0.89-0.94). The IOTA-SR was applicable in 85.7% of adnexal masses.

    CONCLUSION: The IOTA-SR is highly effective in the presurgical differentiation of malignant versus benign adnexal masses when applied by an expert ultrasonography operator.

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