METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).
RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.
CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.
METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.
RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.
CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.
DESIGN: Systematic review and meta-analysis.
SETTING: Electronic search for randomized controlled trials and observational studies (MEDLINE, EMBASE, CENTRAL).
PARTICIPANTS: Hospitalized adults ≥ 18 years old who were SARS-CoV-2 PCR positive.
INTERVENTIONS: High-dose and low-dose corticosteroids.
MEASUREMENTS AND MAIN RESULTS: A total of twelve studies (n=2759 patients) were included in this review. The pooled analysis demonstrated no significant difference in mortality rate between the high-dose and low-dose corticosteroids groups (n=2632; OR: 1.07 [95%CI 0.67, 1.72], p=0.77, I2=76%, trial sequential analysis=inconclusive). No significant differences were observed in the incidence of intensive care unit (ICU) admission rate (n=1544; OR: 0.77[95%CI 0.43, 1.37], p=0.37, I2= 72%), duration of hospital stay (n=1615; MD: 0.53[95%CI -1.36, 2.41], p=0.58, I2=87%), respiratory support (n=1694; OR: 1.51[95%CI 0.77, 2.96], p=0.23, I2=84%), duration of mechanical ventilation (n=419; MD: -1.44[95%CI -4.27, 1.40], p=0.32, I2=93%), incidence of hyperglycemia (n=516, OR: 0.91[95%CI 0.58, 1.43], p=0.68, I2=0%) and infection rate (n=1485, OR: 0.86[95%CI 0.64, 1.16], p=0.33, I2=29%).
CONCLUSION: The meta-analysis demonstrated high-dose corticosteroids did not reduce mortality rate. However, high-dose corticosteroids did not pose higher risk of hyperglycemia and infection rate for COVID-19 patients. Due to the inconclusive trial sequential analysis, substantial heterogeneity and low level of evidence, future large-scale randomized clinical trials are warranted to improve the certainty of evidence for the use of high-dose compared to low-dose corticosteroids in COVID-19 patients.
DESIGN: This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations.
SETTING: General Intensive Care Unit, University of Malaya Medical Centre.
PATIENTS: Mechanically ventilated critically ill patients.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during acute phase (1,765 ± 456 kcal) (p = 0.037). The predictive equations with the best agreement and accuracy for acute phase was Swinamer (1990), for late phase was Brandi (1999) and Swinamer (1990), and for chronic phase was Swinamer (1990). None of the resting energy expenditure calculated from predictive equations showed very good agreement or accuracy.
CONCLUSIONS: Predictive equations tend to either over- or underestimate resting energy expenditure at different phases. Predictive equations with "dynamic" variables and respiratory data had better agreement with resting energy expenditure measured by indirect calorimetry compared with predictive equations developed for healthy adults or predictive equations based on "static" variables. Although none of the resting energy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appears to provide relatively good agreement across three phases and could be used to predict resting energy expenditure when indirect calorimetry is not available.
METHODS: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.
RESULTS: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)-5.6(Age) - 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].
CONCLUSIONS: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.
TRIAL REGISTRATION: ClinicalTrials.gov NCT03319329.
METHODS: In this post hoc analysis of the EFFORT Protein trial, we investigated the effect of high versus usual protein dose (≥ 2.2 vs. ≤ 1.2 g/kg body weight/day) on time-to-discharge alive from the hospital (TTDA) and 60-day mortality and in different subgroups in critically ill patients with AKI as defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria within 7 days of ICU admission. The associations of protein dose with incidence and duration of kidney replacement therapy (KRT) were also investigated.
RESULTS: Of the 1329 randomized patients, 312 developed AKI and were included in this analysis (163 in the high and 149 in the usual protein dose group). High protein was associated with a slower time-to-discharge alive from the hospital (TTDA) (hazard ratio 0.5, 95% CI 0.4-0.8) and higher 60-day mortality (relative risk 1.4 (95% CI 1.1-1.8). Effect modification was not statistically significant for any subgroup, and no subgroups suggested a beneficial effect of higher protein, although the harmful effect of higher protein target appeared to disappear in patients who received kidney replacement therapy (KRT). Protein dose was not significantly associated with the incidence of AKI and KRT or duration of KRT.
CONCLUSIONS: In critically ill patients with AKI, high protein may be associated with worse outcomes in all AKI stages. Recommendation of higher protein dosing in AKI patients should be carefully re-evaluated to avoid potential harmful effects especially in patients who were not treated with KRT.
TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov (NCT03160547) on May 17th 2017.
MATERIALS AND METHODS: A 56-item questionnaire survey on NOA diagnosis and management was conducted globally from July to September 2022. This paper focuses on part 1, evaluating NOA diagnosis. Data from 367 participants across 49 countries were analyzed descriptively, with a Delphi process used for expert recommendations.
RESULTS: Of 336 eligible responses, most participants were experienced attending physicians (70.93%). To diagnose azoospermia definitively, 81.7% requested two semen samples. Commonly ordered hormone tests included serum follicle-stimulating hormone (FSH) (97.0%), total testosterone (92.9%), and luteinizing hormone (86.9%). Genetic testing was requested by 66.6%, with karyotype analysis (86.2%) and Y chromosome microdeletions (88.3%) prevalent. Diagnostic testicular biopsy, distinguishing obstructive azoospermia (OA) from NOA, was not performed by 45.1%, while 34.6% did it selectively. Differentiation relied on physical examination (76.1%), serum hormone profiles (69.6%), and semen tests (68.1%). Expectations of finding sperm surgically were higher in men with normal FSH, larger testes, and a history of sperm in ejaculate.
CONCLUSIONS: This expert survey, encompassing 367 participants from 49 countries, unveils congruence with recommended guidelines in NOA diagnosis. However, noteworthy disparities in practices suggest a need for evidence-based, international consensus guidelines to standardize NOA evaluation, addressing existing gaps in professional recommendations.
MATERIALS AND METHODS: A 56-question online survey covering various aspects of the evaluation and management of NOA was sent to specialists around the globe. This paper analyzes the results of the second half of the survey dealing with the management of NOA. Results have been compared to current guidelines, and expert recommendations have been provided using a Delphi process.
RESULTS: Participants from 49 countries submitted 336 valid responses. Hormonal therapy for 3 to 6 months was suggested before surgical sperm retrieval (SSR) by 29.6% and 23.6% of participants for normogonadotropic hypogonadism and hypergonadotropic hypogonadism respectively. The SSR rate was reported as 50.0% by 26.0% to 50.0% of participants. Interestingly, 46.0% reported successful SSR in <10% of men with Klinefelter syndrome and 41.3% routinely recommended preimplantation genetic testing. Varicocele repair prior to SSR is recommended by 57.7%. Half of the respondents (57.4%) reported using ultrasound to identify the most vascularized areas in the testis for SSR. One-third proceed directly to microdissection testicular sperm extraction (mTESE) in every case of NOA while others use a staged approach. After a failed conventional TESE, 23.8% wait for 3 months, while 33.1% wait for 6 months before proceeding to mTESE. The cut-off of follicle-stimulating hormone for positive SSR was reported to be 12-19 IU/mL by 22.5% of participants and 20-40 IU/mL by 27.8%, while 31.8% reported no upper limit.
CONCLUSIONS: This is the largest survey to date on the real-world medical and surgical management of NOA by reproductive experts. It demonstrates a diverse practice pattern and highlights the need for evidence-based international consensus guidelines.
METHODS: Rats were fed with illicit (a concoction of street ketamine) ketamine in doses of 100 (N=12), or 300 mg/kg (N=12) for four weeks. Half of the rats were sacrificed after the 4-week feeding for necropsy. The remaining rats were taken off ketamine for 8 weeks to allow for any potential recovery of pathological changes before being sacrificed for necropsy. Histopathological examination was performed on the kidney and urinary bladder.
RESULTS: Submucosal bladder inflammation was seen in 67% of the rats fed with 300 mg/kg illicit ketamine. No bladder inflammation was observed in the control and 100 mg/kg illicit ketamine groups. Renal changes, such as interstitial nephritis and papillary necrosis, were observed in rats given illicit ketamine. After ketamine cessation, no inflammation was observed in the bladder of all rats. However, renal inflammation remained in 60% of the rats given illicit ketamine. No dose-effect relationship was established between oral ketamine and changes in the kidneys.
CONCLUSION: Oral ketamine caused pathological changes in the urinary tract, similar to that described in exposure to parenteral ketamine. The changes in the urinary bladder were reversible after short-term exposure.