Traditionally Plumbago rosea L. is used as an abortifacient in the Southeast Asian region. Methanolic root extract of a local species of Plumbago rosea L. was studied to evaluate its traditional antifertility claim. Interestingly, it was found to possess dose related inhibitory effect on uterine contractile responses elicited by oxytocic agents on isolated uteri of pregnant and pseudo-pregnant rats. Furthermore, it was found to possess significant (p < 0.05) fetotoxic activity along with mild abortive potential in pregnant mice when given orally at high doses (400 and 800 mg kg(-1)) once daily for ten days starting from day 10 of gestation. The results derived indicated possible presence of utero-active compound (s) in this plant that inhibited oxytocic agents induced uterine motility. Moreover, pronounced fetotoxic and mild abortifacient potentials observed at higher doses in pregnant mice might support its accredited traditional use to avoid unwanted pregnancy.
Addressing environmental challenges such as pollution and resource depletion requires innovative industrial and municipal waste management approaches. Cement production, a significant contributor to greenhouse gas emissions, highlights the need for eco-friendly building materials to combat global warming and promote sustainability. This study evaluates the simultaneous use of Sugarcane Bagasse Ash (SCBA) and Stone Dust (SD) as partial replacements by volume for cement and sand, respectively, at varying ratios in eco-strength concrete mixes designed for 28 MPa (ES-28) and 34 MPa (ES-34), emphasizing their economic and environmental benefits. The influence of SCBA and SD on workability, mechanical properties, and durability were experimentally investigated. Results reveal that for ES-28, with 9% SCBA and 50% SD, compressive and tensile strengths were nearly equal to the control mix, while flexural strength improved by 6.86%. For ES-34, with 9% SCBA and 50% SD, compressive strength was enhanced by 10.16%, tensile strength by 11.68%, and flexural strength by 5.22%, compared to the control mix. This improvement is attributed to pozzolanic reactions, enhanced particle packing, and optimal curing conditions. However, water absorption increased significantly, with ES-28 showing a 31.61% rise and ES-34 a 22.32% rise when SCBA was 9% and SD was 50%. These results highlight the trade-offs between mechanical performance and durability. The optimized mix, derived from response surface analysis, demonstrates significant potential as a sustainable alternative to conventional concrete, aligning with environmental and structural performance objectives.
Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease's complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models' opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.
In today's modern society, social media has seamlessly integrated into our daily routines, providing a platform for individuals to express their opinions and emotions openly on the internet. Within this digital domain, sentiment analysis (SA) is a vital tool to understand the emotions conveyed in written text, whether positive, negative, or neutral. However, SA faces challenges such as dealing with diverse language, uneven data, and understanding complex sentences. This study proposes an effective approach for SA. For this, we introduce a hybrid architecture named DistilRoBiLSTMFuse, designed to extract deep contextual information from complex sentences and accurately identify sentiments. In this research, we evaluate our model's performance using two popular benchmark datasets: IMDb and Twitter USAirline sentiment. The raw text data are preprocessed, and this involves several steps, including: (1) implementing a comprehensive data cleaning protocol to remove noise and unnecessary information from the raw text, (2) preparing a custom list of stopwords to retain essential words while omitting common, non-informative words, and (3) applying Lemmatization to achieve consistency in text by reducing words to their base forms, enhancing the accuracy of text analysis. To address class imbalance, this study utilized oversampling, augmenting minority class samples to match the majority, thereby ensuring uniform representation across all categories. Considering the variability in preprocessing techniques across previous studies, our research initially explores the efficacy of seven distinct machine learning (ML) models paired with two commonly employed feature transformation methods: term frequency-inverse document frequency (TF-IDF) and bag of words (BoW). This approach allows for determining which combination yields optimal performance within these ML frameworks. In our study, the DistilRoBiLSTMFuse model is evaluated on two distinct datasets and consistently delivers outstanding performance, surpassing existing state-of-the-art approaches in each case. On the IMDb dataset, our model achieves 98.91% accuracy in training, 94.16% in validation, and 93.97% in testing. The Twitter USAirline Sentiment dataset reaches 99.42% accuracy in training, 98.52% in validation, and 98.33% in testing. The experimental results clearly demonstrate the effectiveness of our hybrid DistilRoBiLSTMFuse model in SA tasks. The code for this experimental analysis is publicly available and can be accessed via the following DOI: https://doi.org/10.5281/zenodo.13255008.
Experiments were conducted to identify blast-resistant fragrant genotypes for the development of a durable blast-resistant rice variety during years 2012-2013. The results indicate that out of 140 test materials including 114 fragrant germplasms, 25 differential varieties (DVs) harbouring 23 blast-resistant genes, only 16 fragrant rice germplasms showed comparatively better performance against a virulent isolate of blast disease. The reaction pattern of single-spore isolate of Magnaporthe oryzae to differential varieties showed that Pish, Pi9, Pita-2 and Pita are the effective blast-resistant genes against the tested blast isolates in Bangladesh. The DNA markers profiles of selected 16 rice germplasms indicated that genotype Chinigura contained Pish, Pi9 and Pita genes; on the other hand, both BRRI dhan50 and Bawaibhog contained Pish and Pita genes in their genetic background. Genotypes Jirakatari, BR5, and Gopalbhog possessed Pish gene, while Uknimodhu, Deshikatari, Radhunipagol, Kalijira (3), Chinikanai each contained the Pita gene only. There are some materials that did not contain any target gene(s) in their genetic background, but proved resistant in pathogenicity tests. This information provided valuable genetic information for breeders to develop durable blast-resistant fragrant or aromatic rice varieties in Bangladesh.
α(1D)-adrenoceptors are involved in the genesis/maintenance of hypertension in spontaneously hypertensive rats (SHR). This study aims to investigate the role of α(1D)-adrenoceptors in the antinatriuretic and antidiuretic responses in SHR subjected to high sodium (SHRHNa) and normal sodium (SHRNNa) intake for six weeks. Renal inulin clearance study was performed in which the antinatriuretic and antidiuretic responses to phenylephrine were examined in the presence and absence of α(₁D)-adrenoceptors blocker BMY7378. Data, mean±S.E.M. were subjected to ANOVA with significance at p<0.05. Results show that feeding SHR for six weeks with high salt did not cause any change in blood pressure. SHRHNa had higher (all p<0.05) urine flow rate (UFR), fractional and absolute excretion of sodium (FE(Na) and U(Na)V) compared to SHRNNa. Phenylephrine infusion produced significant reduction in UFR, FE(Na) and U(Na)V in both SHRHNa and SHRNNa. The antidiuretic and antinatriuretic responses to phenylephrine in both groups were attenuated in the presence of BMY7378. Moreover, the antidiuretic and antinatriuretic responses to phenylephrine and BMY7378 were independent on any significant changes in renal and glomerular hemodynamics in both groups. Thus we conclude that high sodium intake did not bring any further increase in blood pressure of SHR, however, it results in exaggerated natriuresis and diuresis in SHRHNa. Irrespective of dietary sodium changes, α₁-adrenoceptors are involved in mediating the antinatriuretic and antidiuretic responses to phenylephrine in SHR. Further, high sodium intake did not significantly influence the functionality of α(₁D)-adrenoceptors in mediating the adrenergically induced antinatriuresis and antidiuresis.
The role of renal sympathetic nerves in the pathogenesis of ischemic acute renal failure (ARF) and the immediate changes in the renal excretory functions following renal ischemia were investigated. Two groups of male Sprague Dawley (SD) rats were anesthetized (pentobarbitone sodium, 60 mg kg(-1) i.p.) and subjected to unilateral renal ischemia by clamping the left renal artery for 30 min followed by reperfusion. In group 1, the renal nerves were electrically stimulated and the responses in the renal blood flow (RBF) and renal vascular resistance (RVR) were recorded, while group 2 was used to study the early changes in the renal functions following renal ischemia. In post-ischemic animals, basal RBF and the renal vasoconstrictor reperfusion to renal nerve stimulation (RNS) were significantly lower (all p < 0.05 vs. control). Mean arterial pressure (MAP), basal RVR, urine flow rate (UFR), absolute and fractional excretions of sodium (U(Na)V and FE(Na)), and potassium (U(K)V and FE(K)) were higher in ARF rats (all p < 0.05 vs. control). Post-ischemic animals showed markedly lower glomerular filtration rate (GFR) (p < 0.05 vs. control). No appreciable differences were observed in urinary sodium to potassium ratio (U(Na)/U(K)) during the early reperfusion phase of renal ischemia (p > 0.05 vs. control). The data suggest an immediate involvement of renal sympathetic nerve action in the pathogenesis of ischemic ARF primarily through altered renal hemodynamics. Diuresis, natriuresis, and kaliuresis due to impaired renal tubular functions are typical responses to renal ischemia and of comparable magnitudes.
Biofilms are adherent, multi-layered colonies of bacteria that are typically more resistant to the host immune response and routine antibiotic therapy. HA biomaterial comprises of a single-phased hydroxyapatite scaffold with interconnected pore structure. The device is designed as osteoconductive space filler to be gently packed into bony voids or gaps following tooth extraction or any surgical procedure. Gentamycin-coated biomaterial (locally made hydroxyapatite) was evaluated to reduce or eradicate the biofilm on the implant materials. The results indicated that the HA coated with gentamycin was biocompatible to human osteoblast cell line and the biofilm has been reduced after being treated with different concentrations of gentamycin-coated hydroxyapatite (HA).
1. This study was undertaken to elucidate the effects of inhibiting the renin-angiotensin system (RAS) with losartan, and acute unilateral renal denervation on renal haemodynamic responses to intrarenal administration of vasoconstrictor doses of dopamine and vasodilator doses of isoprenaline in Wistar-Kyoto (WKY) and spontaneously hypertensive rats (SHR). 2. Acute unilateral renal denervation of the left kidney in rats was confirmed by a drop in the renal vasoconstrictor response to renal nerve stimulation (P < 0.05) along with diuresis and natriuresis. Rats were pretreated with losartan for 7 days and thereafter animals fasted overnight were anaesthetized (sodium pentobarbitone, 60 mg/kg i.p.) and acute renal haemodynamic responses studied. 3. Dose-response curves were constructed for dopamine and isoprenaline that induced falls or increases in renal blood flow, respectively. It was observed that renal vascular responses were greater in the denervated as compared with rats with intact renal nerves (all P < 0.05). Dopamine-induced renal vasoconstrictor responses were markedly lower in losartan-treated denervated WKY and SHR compared with their untreated counterparts (all P < 0.05). It was also observed that in losartan-treated and denervated WKY rats the vasodilatory responses to isoprenaline were markedly lower compared with untreated rats (all P < 0.05). However, in SHR, under the same conditions, there was no difference in the renal response to isoprenaline whether or not rats were treated with losartan (P > 0.05). 4. The data obtained showed that the renal vasoconstrictor effect of dopamine depends on intact renal nerves and RAS in WKY and SHR. Isoprenaline responses were likewise sensitive to renal denervation and RAS inhibition in WKY rats but not SHRs. Our observations reveal a possible relationship between renal AT(1) receptors and alpha(1)-adrenoceptors in WKY and SHR. There is also evidence to suggest an interaction between renal beta-adrenoceptors and AT(1) receptors in WKY rats.
Limited resources of healthcare system and high use of antidepressants have raised some serious concerns regarding proper surveillance system of prescribed medicines. Not much literature is available from Pakistan regarding the potential drug-drug interactions (pDDIs) associated with antidepressants.
A series of thirty (30) thiazole analogs were prepared, characterized by (1)H NMR, (13)C NMR and EI-MS and evaluated for Acetylcholinesterase and butyrylcholinesterase inhibitory potential. All analogs exhibited varied butyrylcholinesterase inhibitory activity with IC50 value ranging between 1.59±0.01 and 389.25±1.75μM when compared with the standard eserine (IC50, 0.85±0.0001μM). Analogs 15, 7, 12, 9, 14, 1, 30 with IC50 values 1.59±0.01, 1.77±0.01, 6.21±0.01, 7.56±0.01, 8.46±0.01, 14.81±0.32 and 16.54±0.21μM respectively showed excellent inhibitory potential. Seven analogs 15, 20, 19, 24, 28, 30 and 25 exhibited good acetylcholinesterase inhibitory potential with IC50 values 21.3±0.50, 35.3±0.64, 36.6±0.70, 44.81±0.81, 46.36±0.84, 48.2±0.06 and 48.72±0.91μM respectively. All other analogs also exhibited well to moderate enzyme inhibition. The binding mode of these compounds was confirmed through molecular docking.
This study examined the effects of creep feed (CF) supplementation (with or without Alfalfa) on the pre-weaning growth performance of nursing goat kids.
Antimicrobial resistance is of concern to global health security worldwide. We aimed to identify the prevalence, resistance patterns, and risk factors associated with Escherichia coli (E. coli) resistance from poultry farms in Kelantan, Terengganu, and Pahang states of east coast peninsular Malaysia. Between 8 February 2019 and 23 February 2020, a total of 371 samples (cloacal swabs = 259; faecal = 84; Sewage = 14, Tap water = 14) were collected. Characteristics of the sampled farms including management type, biosecurity, and history of disease were obtained using semi-structured questionnaire. Presumptive E. coli isolates were identified based on colony morphology with subsequent biochemical and PCR confirmation. Susceptibility of isolates was tested against a panel of 12 antimicrobials and interpreted alongside risk factor data obtained from the surveys. We isolated 717 E. coli samples from poultry and environmental samples. Our findings revealed that cloacal (17.8%, 46/259), faecal (22.6%, 19/84), sewage (14.3%, 2/14) and tap water (7.1%, 1/14) were significantly (p < 0.003) resistant to at least three classes of antimicrobials. Resistance to tetracycline class were predominantly observed in faecal samples (69%, 58/84), followed by cloacal (64.1%, 166/259), sewage (35.7%, 5/14), and tap water (7.1%, 1/84), respectively. Sewage water (OR = 7.22, 95% CI = 0.95-151.21) had significant association with antimicrobial resistance (AMR) acquisition. Multivariate regression analysis identified that the risk factors including sewage samples (OR = 7.43, 95% CI = 0.96-156.87) and farm size are leading drivers of E. coli antimicrobial resistance in the participating states of east coast peninsular Malaysia. We observed that the resistance patterns of E. coli isolates against 12 panel antimicrobials are generally similar in all selected states of east coast peninsular Malaysia. The highest prevalence of resistance was recorded in tetracycline (91.2%), oxytetracycline (89.1%), sulfamethoxazole/trimethoprim (73.1%), doxycycline (63%), and sulfamethoxazole (63%). A close association between different risk factors and the high prevalence of antimicrobial-resistant E. coli strains reflects increased exposure to resistant bacteria and suggests a concern over rising misuse of veterinary antimicrobials that may contribute to the future threat of emergence of multidrug-resistant pathogen isolates. Public health interventions to limit antimicrobial resistance need to be tailored to local poultry farm practices that affect bacterial transmission.
We assessed the role of renal sympathetic nervous system in the deterioration of renal hemodynamic and excretory functions in rats with streptozotocin (STZ)-induced diabetic kidney disease (DKD).
The burden of antimicrobial use in agricultural settings is one of the greatest challenges facing global health and food security in the modern era. Malaysian poultry operations are a relevant but understudied component of epidemiology of antimicrobial resistance. We aimed to identify the prevalence, resistance patterns, and risk factors associated with Salmonella isolates from poultry farms in three states of East Coast Peninsular Malaysia. Between 8 February 2019 and 23 February 2020, a total of 371 samples (cloacal swabs = 259; faecal = 84; Sewage = 14, Tap water = 14) was collected from poultry operations. Characteristics of the sampled farms and associated risk factors were obtained using semi-structured questionnaires. Presumptive Salmonella spp. isolates were identified based on colony morphology with subsequent biochemical and PCR confirmation. Susceptibility of isolates was tested against a panel of 12 antimicrobials using disk diffusion method. Our findings revealed that the proportion of Salmonella spp.-positive isolates across sample source were as following: cloacal swab (46.3%, 120/259); faecal (59.5%, 50/84); in tap water (14.3%, 2/14); and in sewage sample (35.7%, 5/14). Isolates from faecal (15.5%, 13/84), cloacal (1.2%, 3/259), and sewage (7.1%, 1/14) samples were significantly resistant to at least five classes of antimicrobials. Resistance to Sulfonamides class (52%, 92/177) was predominantly observed followed by tetracycline (39.5%, 70/177) and aminoglycosides (35.6%, 63/177). Multivariate regression analysis identified intensive management system (OR = 1.55, 95% CI = 1.00-2.40) as a leading driver of antimicrobial resistance (AMR) acquisition. A prevalence of resistance to common antimicrobials was recorded for sulfamethoxazole (33.9%), tetracycline (39.5%), and trimethoprim-sulphamethoxazole (37.9%). A close association between different risk factors and the prevalence of AMR of Salmonella strains suggests a concern over rising misuse of veterinary antimicrobials that may contribute to the emergence and evolution of multidrug-resistant pathogen isolates. One Health approach is recommended to achieve a positive health outcome for all species.
Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.
Synthesis of new Cefpodoxime derivatives via Schiff Bases mechanism and the efficiency of their antimicrobial and antiviral activities were addressed. They were analyzed for structural validation by using spectroscopic techniques using FTIR, 1HNMR, and 13CNMR. Molecular docking against IBV Virus papain-like protease (PLPro) was done with Auto dock tools against compounds having excellent IC50 values against IBV (Corona Class) virus. All derivatives showed strong zone of inhibition ranges from (55 ± 2.0 to 70 ± 0.8 mm) against E. coli. Compounds 1,2,4 and 6 derivatives showed remarkable activity against Stenotrophomonas maltophilia and Serratia marcescens. But For most the newly synthesized derivatives C 1 (64 ± 1.60), C 3 (32 ± 0.80), and C 8 (64 ± 1.60) showed potential IC50 values against two variants of Corona class viruses i.e. Avian Influenza (H9) and Avian corona (IBV) viruses. The current study revealed that newly synthesized Schiff Bases possessed strong anti-viral potential. Further studies may make a breakthrough in medical sciences to tackle latest challenges such as Corona Virus Diseases.
The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.
A substantive approach converting waste date pits to mercerized mesoporous date pit activated carbon (DPAC) and utilizing it in the removal of Cd(II), Cu(II), Pb(II), and Zn(II) was reported. In general, rapid heavy metals adsorption kinetics for Co range: 25-100 mg/L was observed, accomplishing 77-97% adsorption within 15 min, finally, attaining equilibrium in 360 min. Linear and non-linear isotherm studies revealed Langmuir model applicability for Cd(II) and Pb(II) adsorption, while Freundlich model was fitted to Zn(II) and Cu(II) adsorption. Maximum monolayer adsorption capacities (qm) for Cd(II), Pb(II), Cu(II), and Zn(II) obtained by non-linear isotherm model at 298 K were 212.1, 133.5, 194.4, and 111 mg/g, respectively. Kinetics modeling parameters showed the applicability of pseudo-second-order model. The activation energy (Ea) magnitude revealed physical nature of adsorption. Maximum elution of Cu(II) (81.6%), Zn(II) (70.1%), Pb(II) (96%), and Cd(II) (78.2%) were observed with 0.1 M HCl. Thermogravimetric analysis of DPAC showed a total weight loss (in two-stages) of 28.3%. Infra-red spectral analysis showed the presence of carboxyl and hydroxyl groups over DPAC surface. The peaks at 820, 825, 845 and 885 cm-1 attributed to Zn-O, Pb-O, Cd-O, and Cu-O appeared on heavy metals saturated DPAC, confirmed their binding on DPAC during the adsorption.
This study was aimed to enhance the dissolution rate, oral bioavailability and analgesic potential of the aceclofenac (AC) in the form of nanosuspension using cost-effective simple precipitation-ultrasonication approach. The nanocrystals were produced using the optimum conditions investigated for AC. The minimum particle size (PS) and polydispersity index was found to be 112±2.01 nm and 0.165, respectively, using hydroxypropyl methylcellulose (1%, w/w), polyvinylpyrrolidone K30 (1%, w/w) and sodium lauryl sulfate (0.12%, w/w). The characterization of AC was performed using zeta sizer, scanning electron microscopy, transmission electron microscopy, powder X-ray diffraction and differential scanning calorimetry. The saturation solubility of the AC nanocrystals was substantially increased 2.6- and 4.5-fold compared to its unprocessed active pharmaceutical ingredient in stabilizer solution and unprocessed drug. Similarly, the dissolution rate of the AC nanocrystals was substantially enhanced compared to its other counterpart. The results showed that >88% of AC nanocrystals were dissolved in first 10 min compared to unprocessed AC (8.38%), microsuspension (66.65%) and its marketed tablets (17.65%). The in vivo studies of the produced stabilized nanosuspension demonstrated that the Cmax were 4.98- and 2.80-fold while area under curve from time of administration to 24 h (AUC0→24 h) were found 3.88- and 2.10-fold greater when compared with unprocessed drug and its marketed formulation, respectively. The improved antinociceptive activity of AC nanocrystals was shown at much lower doses as compared to unprocessed drug, which is purely because of nanonization which may be attributed to improved solubility and dissolution rate of AC, ultimately resulting in its faster rate of absorption.