The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.

BACKGROUND: The utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined. METHOD: Retrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables. RESULTS: Four thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976. CONCLUSION: Our study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost-benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population.

The crucial relationship between vancomycin minimum inhibitory concentration and therapeutic efficacy against methicillin-resistant coagulase-negative staphylococci.

The area under the curve (AUC)/minimum inhibitory concentration (MIC) ratio was used as an indicator of the clinical efficacy of vancomycin. However, the target AUC/MIC has not been set for methicillin-resistant coagulase-negative staphylococci (MR-CNS), and the effectiveness of vancomycin in strains with high MIC is unknown. Therefore, we aimed to investigate the relationship between the vancomycin MIC and therapeutic efficacy in patients with MR-CNS bacteremia. The primary outcome was the difference in treatment failure rate when the MR-CNS vancomycin MIC was 1 or 2 µg/mL. The treatment failure rate did not significantly differ between the two groups (MIC 1 vs. MIC 2: 27.0% vs. 31.0%; p = 0.779). As a result of multivariate analysis, AUC/MIC0-24 h ≤230 was extracted as risk factor for treatment failure, suggesting the importance of a sufficient initial loading dose and early blood concentration monitoring to increase AUC/MIC0-24 h for successful treatment.

Novel Genes Associated with Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Scores and PheWAS-Derived Risk Factors.

BACKGROUND: Atrial fibrillation (AF), the most common atrial arrhythmia, presents varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. This study aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a Genome-Wide Association Study (GWAS) on a substantial cohort of Taiwanese individuals, evaluating the models' predictive efficacy. METHODS: Involving 75,121 subjects, including 5,694 AF patients and 69,427 normal controls with GWAS data, the study merged polygenic risk scores (PRS) from AF-associated SNPs with Phenome-wide association study (PheWAS)-derived risk factors. Advanced statistical and machine learning techniques were employed to develop and evaluate AF predictive models for discrimination and calibration. RESULTS: The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using PRS-CS analysis for AF exhibited an area under the curve (AUC) of 0.600 (P < 0.001), improving to 0.855 (P < 0.001) after adjusting for age and gender. PheWAS analysis showed the top 10 diseases associated with these genes were circulatory system diseases. CONCLUSIONS: Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.

Prediction of neonatal outcomes using gestational age vs ACOG definitions of maternal disease severity in hypertensive disorders of pregnancy.

PURPOSE: Hypertensive disorders of pregnancy cause significant neonatal complications. Disease severity is often used to predict neonatal outcomes, however gestational age (GA) at delivery may be a better predictor. We aimed to assess whether disease severity or GA was more predictive of adverse neonatal outcomes. METHODS: We included 165 participants with confirmed HELLP syndrome or severe preeclampsia (sPE). Two predictive models were constructed to assess the ability of disease severity compared to GA to predict a composite adverse neonatal outcome. The composite outcome included low birth weight, SGA, IUGR, Apgar score, and neonatal death. RESULTS: Using severity as a predictor of binary neonatal outcome had an AUC of 0.73 (0.65-0.81), with a sensitivity (SE) of 70.3% and a specificity (SP) of 64.4%. For GA, we observed an AUC of 0.82 (0.75-0.89), with a SE of 75.7% and a SP of 76.7%. CONCLUSION: For the composite neonatal outcome, GA was a better predictor than ACOG diagnosis (severity). This observation underscores the need for further research to validate these findings in larger cohorts and to determine their applicability to maternal outcomes.

Lifelog-based daily step counts, walking speed, and metabolically healthy status.

Objective: Optimal metabolically healthy status is important to prevent various chronic diseases. This study investigated the association between lifelog-derived physical activity and metabolically healthy status. Methods: This cross-sectional study included 51 Korean adults aged 30-40 years with no history of chronic diseases. Physical activity data were obtained by the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Lifelog-derived physical activity was defined by step counts and walking speed for 1 week, as recorded by the Samsung Health application on both the Samsung Galaxy Fit2 and mobile phones. Participants without metabolic syndrome components were categorized as the metabolically healthy group (n = 31) and the remaining participants as the metabolically unhealthy group (n = 20). Prevalence ratios and 95% confidence intervals were estimated using Poisson regression models. The predictive ability of each physical activity measure was evaluated according to the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) values. Results: Among the physical activity measures, lifelog-derived walking speed was significantly inversely associated with prevalent metabolically unhealthy status. The lifelog component model including walking speed, age, and sex had the highest AUC value for metabolically unhealthy status. Adding lifelog-derived step counts to the IPAQ-SF-derived metabolic equivalent (MET) model (including age, sex, and IPAQ-SF-METs) yielded 37% and 13% increases in the NRI and IDI values, respectively. Incorporating walking speed into the IPAQ-SF-derived MET model improved metabolically unhealthy status prediction by 42% and 21% in the NRI and IDI analyses, respectively. Conclusions: Slow walking speed derived from the lifelog was associated with a higher prevalence of metabolically unhealthy status. Lifelog-derived physical activity information may aid in identifying individuals with metabolic abnormalities.

Accuracy of salivary biomarkers in the diagnosis of periodontal status and coronary heart disease.

Inflammatory illnesses, such as periodontitis and atherosclerotic coronary heart disease (ASCHD), trigger the production of pro-inflammatory mediators. The aim of this study was to assess the accuracy of using salivary interleukin-1ß (IL-1ß), interleukin-18 (IL-18), and gasdermin D (GSDMD) in discerning patients with periodontitis with and without ASCHD from healthy individuals, and to assess their correlation with clinical periodontal parameters and low-density lipoprotein (LDL) levels. The study involved 120 participants: 30 were healthy subjects (control group, C), 30 had generalized periodontitis (group P), 30 had ASCHD and clinically healthy periodontium (group AS-C), and 30 had ASCHD and generalized periodontitis (group AS-P). Saliva and blood samples were collected, and periodontal characteristics such as plaque index, bleeding on probing, probing pocket depth, and clinical attachment loss were examined. IL-1ß, IL-18, and GSDMD levels from saliva were determined using ELISA. LDL levels were determined from the blood samples. Groups P, AS-C, and AS-P had higher levels of salivary IL-1ß, IL-18, and GSDMD than group C. The receiver operating characteristic (ROC) curves of all biomarkers showed high diagnostic accuracy, with a significant positive correlation with the clinical parameters and LDL levels. The observed correlations between the studied pro-inflammatory mediators and disease severity suggest that these biomarkers could serve as indicators of disease progression in conditions such as periodontitis and ASCHD.

"What's yours is mine": Partners' everyday emotional experiences and cortisol in older adult couples.

The existing literature consistently finds that emotional experiences and cortisol secretion are linked at the within-person level. Further, relationship partners tend to covary in emotional experience, and in cortisol secretion. However, we are only beginning to understand whether and how an individuals' emotions are linked to their relationship partners' cortisol secretion. In this project, we harmonized data from three intensive measurement studies originating from Canada and Germany to investigate the daily dynamics of emotions and cortisol within 321 older adult couples (age range=56-87 years). Three-level multilevel models accounted for the nested structure of the data (repeated assessments within individuals within couples). Actor-Partner Interdependence Models were used to examine the effect of own emotional experiences (actor effects) and partner emotional experiences (partner effects) on momentary and daily cortisol secretion. Adjusting for age, sex, education, comorbidities, assay version, diurnal cortisol rhythm, time spent together, medication, and time-varying behaviors that may increase cortisol secretion, results suggest that higher relationship partner's positive emotions are linked with lower momentary cortisol and total daily cortisol. Further, this association was stronger for older participants and those who reported higher relationship satisfaction. We did not find within-couple links between negative emotions and cortisol. Overall, our results suggest that one's relationship partner's positive emotional experience may be a protective factor for their physiological responding, and that these more fleeting and day-to-day fluctuations may accumulate over time, contributing to overall relationship satisfaction.

Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae.

OBJECTIVES: Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. METHODS: This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. RESULTS: Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. CONCLUSIONS: The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Construction of an in vitro simulated one compartment extravascular administration model and its comparison with classic in vitro administration model in copper chloride induced HepG2 cell death.

In this study, we designed an in vitro administration device based on compartment model theory and utilized it to construct an in vitro simulated one compartment extravascular administration model of copper chloride. Within the Cmax range of 3.91-1000.00 µM, the measured concentration-time curves of the simulated one compartment extravascular administration model almost coincide with the corresponding theoretical curves. The measured values of toxicokinetic parameters, including ke, T1/2, ka, T1/2a, Tmax, Cmax, CL, and AUC0-∞ are close to the corresponding theoretical values. The fitting coefficients are >0.9990. In simulated one compartment extravascular administration and classic in vitro administration, copper chloride dose-dependently induced HepG2 cell death. When Cmax/administration concentration is equal, classic in vitro administration induces a higher cell death rate than simulated one compartment extravascular administration. However, there is no significant difference in inducing cell death between the two administration models when area under the curve is equal. In conclusion, the device designed in this study can be used to in vitro simulate one compartment extravascular administration, making in vitro toxicity testing more similar to in vivo scenarios. There are differences in copper chloride induced HepG2 cell death between simulated one compartment extravascular administration and classic in vitro administration.

Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

Characterizing Day 1 Area Under the Curve Following Vancomycin Loading Dose Administration in Adult Hospitalized Patients Using Non-Trapezoidal Linear Pharmacokinetic Equations: A Retrospective Observational Study.

INTRODUCTION: Methicillin-resistant Staphylococcus aureus (MRSA) infections are a serious threat to public health. Vancomycin (VAN) remains the primary treatment for these infections, and achieving the recommended area under the curve (AUC) target has been linked to improved clinical outcomes. The current VAN therapeutic monitoring guidelines recommend a loading dose (LD) of 20-35 mg/kg to rapidly attain targeted VAN exposures within 24 h of therapy. However, there is a paucity of data describing the impact of VAN LD on day 1 area under the curve (AUC0-24). This study aims to employ pharmacokinetic (PK) equations to calculate and describe the AUC0-24 following a VAN LD of 20 mg/kg. METHODS: This was a retrospective study of adult patients who were loaded with VAN 20 mg/kg, received ≥ 48 h of treatment, and had two consecutive serum VAN levels collected within 24 h. Linear, non-trapezoidal PK equations and two post-infusion VAN levels were used to calculate AUC0-24. Therapeutic AUC0-24 was defined as 400-600 mg/l*h. RESULTS: Among 123 included patients, the median age was 46 years (IQR 36, 62), 54% (67/123) of the patients had a body mass index (BMI) ≥ 30 kg/m2 and 27% (33/123) were admitted to the intensive care unit (ICU). Following a LD of 20 mg/kg, 50% (61/123) of the patients met the therapeutic AUC0-24, while 22% (27/123) of the patients were subtherapeutic, and 28% (35/123) were supratherapeutic. Compared with patients who achieved therapeutic AUC0-24, patients with subtherapeutic AUC0-24 were more likely to be younger (44 vs. 37 years old) and have a BMI ≥ 30 kg/m2 (67 vs. 52%). In contrast, patients with supratherapeutic AUC0-24 were more likely to be older (64 vs. 44 years old) and to have chronic kidney disease diagnosis (23 vs. 7%) when compared to patients who achieved a therapeutic AUC0-24. CONCLUSIONS: Only 50% of patients achieve the target AUC0-24 following a VAN 20 mg/kg LD, with younger, heavier patients underexposed and older patients with renal impairment overexposed, suggesting that different dosing strategies are needed for these populations.

Value of turbo spin echo-based diffusion-weighted imaging in the differential diagnosis of benign and malignant solitary pulmonary lesions.

To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)-based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE-based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann-Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = - 4.522, p < 0.001). Additionally, the ADC800, ADCtotal, and D values were all significantly lower in lung cancer than in the benign lesions (Z = - 5.054, -5.370, and -6.047, respectively, all p < 0.001), whereas the f values did not exhibit any statistically significant difference between the two groups. D had the highest area under the curve (AUC = 0.887), followed by ADCtotal (AUC = 0.844), ADC800 (AUC = 0.824), and LSR (AUC = 0.789). The LSR, ADC800, ADCtotal, and D values did not differ statistically significantly in diagnostic effectiveness. Lung DWI using TSE is feasible for differentiating SPLs. The LSR method, conventional DWI, and IVIM have comparable diagnostic efficacy for assessing SPLs.

Avapritinib Carries the Risk of Drug Interaction via Inhibition of UDP-Glucuronosyltransferase (UGT) 1A1.

BACKGROUND: Avapritinib is the only drug for adult patients with PDGFRA exon 18 mutated unresectable or metastatic gastrointestinal stromal tumor (GIST). Although avapritinib has been approved by the FDA for four years, little is known about the risk of drug-drug interactions (DDIs) via UDP-glucuronyltransferases (UGTs) inhibition. OBJECTIVE: The aim of the present study was to systematically evaluate the inhibitory effects of avapritinib against UGTs and to quantitatively estimate its potential DDIs risk in vivo. METHODS: Recombinant human UGTs were employed to catalyze the glucuronidation of substrates in a range of concentrations of avapritinib. The kinetics analysis was performed to evaluate the inhibition types of avapritinib against UGTs. The quantitative prediction of DDIs was done using in vitro-in vivo extrapolation (IVIVE). RESULTS: Avapritinib had a potent competitive inhibitory effect on UGT1A1. Quantitative prediction results showed that avapritinib administered at clinical doses might result in a 14.85% in-crease in area under the curve (AUC) of drugs primarily cleared by UGT1A1. Moreover, the Rgut value was calculated to be 18.44. CONCLUSION: Avapritinib has the potential to cause intestinal DDIs via the inhibition of UGT1A1. Additional attention should be paid when avapritinib is coadministered with UGT1A1 substrates.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank.

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Gut microbiota predicts the diagnosis of ulcerative colitis in Saudi children.

BACKGROUND: Ulcerative colitis (UC) is an immune-mediated chronic inflammatory condition with a worldwide distribution. Although the etiology of this disease is still unknown, the understanding of the role of the microbiota is becoming increasingly strong. AIM: To investigate the predictive power of the gut microbiota for the diagnosis of UC in a cohort of newly diagnosed treatment-naïve Saudi children with UC. METHODS: The study population included 20 children with a confirmed diagnosis of UC and 20 healthy controls. Microbial DNA was extracted and sequenced, and shotgun metagenomic analysis was performed for bacteria and bacteriophages. Biostatistics and bioinformatics demonstrated significant dysbiosis in the form of reduced alpha diversity, beta diversity, and significant difference of abundance of taxa between children with UC and control groups. The receiver operating characteristic curve, a probability curve, was used to determine the difference between the UC and control groups. The area under the curve (AUC) represents the degree of separability between the UC group and the control group. The AUC was calculated for all identified bacterial species and for bacterial species identified by the random forest classification algorithm as important potential biomarkers of UC. A similar method of AUC calculation for all bacteriophages and important species was used. RESULTS: The median age and range were 14 (0.5-21) and 12.9 (6.8-16.3) years for children with UC and controls, respectively, and 40% and 35% were male for children with UC and controls, respectively. The AUC for all identified bacterial species was 89.5%. However, when using the bacterial species identified as important by random forest classification algorithm analysis, the accuracy increased to 97.6%. Similarly, the AUC for all the identified bacteriophages was 87.4%, but this value increased to 94.5% when the important bacteriophage biomarkers were used. CONCLUSION: The very high to excellent AUCs of fecal bacterial and viral species suggest the potential use of noninvasive microbiota-based tests for the diagnosis of unusual cases of UC in children. In addition, the identification of important bacteria and bacteriophages whose abundance is reduced in children with UC suggests the potential of preventive and adjuvant microbial therapy for UC.

Effect of liposomal bupivacaine for preoperative erector spinae plane block on postoperative pain following video-assisted thoracoscopic lung surgery: a protocol for a multicenter, randomized, double-blind, clinical trial.

Background: There is still a controversy about the superiority of liposomal bupivacaine (LB) over traditional local anesthetics in postoperative analgesia after thoracic surgery. This study aims to determine the effect of LB versus bupivacaine hydrochloride (HCl) for preoperative ultrasound-guided erector spinae plane block (ESPB) on postoperative acute and chronic pain in patients undergoing video-assisted thoracoscopic lung surgery. Methods: This multicenter, randomized, double-blind, controlled trial will include 272 adult patients scheduled for elective video-assisted thoracoscopic lung surgery. Patients will be randomly assigned, 1:1 and stratified by site, to the liposomal bupivacaine (LB) group or the bupivacaine (BUPI) HCl group. All patients will receive ultrasound-guided ESPB with either LB or bupivacaine HCl before surgery and patient-controlled intravenous analgesia (PCIA) as rescue analgesia after surgery. The numeric rating scale (NRS) score will be assessed after surgery. The primary outcome is the area under the curve of pain scores at rest for 0-72 h postoperatively. The secondary outcomes include the total amount of opioid rescue analgesics through 0-72 h postoperatively, time to the first press on the PCIA device as rescue analgesia, the area under the curve of pain scores on activity for 0-72 h postoperatively, NRS scores at rest and on activity at different time points during the 0-72 h postoperative period, Quality of Recovery 15 scores at 72 h after surgery, and NRS scores on activity on postsurgical day 14 and postsurgical 3 months. Adverse events after the surgery are followed up to the postsurgical day 7, including postoperative nausea and vomiting, fever, constipation, dizziness, headache, insomnia, itching, prolonged chest tube leakage, new-onset atrial fibrillation, severe ventricular arrhythmia, deep venous thrombosis, pulmonary embolism, pulmonary atelectasis, cardiac arrest, ileus, urinary retention, chylothorax, pneumothorax, and organ failure. Analyzes will be performed first according to the intention to treat principle and second with the per-protocol analysis. Discussion: We hypothesize that LB for preoperative ultrasound-guided ESPB would be more effective than bupivacaine HCl in reducing postoperative pain in video-assisted thoracoscopic lung surgery. Our results will contribute to the optimization of postoperative analgesia regimens for patients undergoing video-assisted thoracoscopic lung surgery.Clinical trial registration:http://www.chictr.org.cn, identifier ChiCTR2300074852.

Correlation between trough concentration and AUC for elexacaftor, tezacaftor and ivacaftor.

Therapeutic drug monitoring (TDM) of elexacaftor, tezacaftor, ivacaftor (ETI) could be a useful tool to increase efficacy and decrease the risk of adverse effects in people with Cystic Fibrosis (pwCF). It is however unclear whether drug exposure should be monitored by assessment of trough (Cmin) levels or determination of the area under the curve (AUC). Hence, in this study the correlation between measured Cmin concentration and AUC was evaluated. Serial plasma samples, including Cmin, were drawn after administration of ETI in order to calculate the AUC and assess the correlation between the two parameters. A linear correlation between Cmin and AUC0-24h was found, with Pearson's r correlation coefficients of 0.963, 0.908 and 0.860 for elexacaftor, tezacaftor and ivacaftor, respectively. Exposure of ETI may be monitored by assessment of Cmin levels.

ANN multi-layer perceptron for prediction of blood-brain barrier permeable compounds for central nervous system therapeutics.

Endothelial cells produce a semipermeable barrier known as the blood-brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study's outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.Communicated by Ramaswamy H. Sarma.

A novel approach to model cumulative stress: Area under the s-factor curve.

OBJECTIVE: Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes. METHOD: We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes. RESULTS: The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change. CONCLUSIONS: Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.