Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA). We built three random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified two distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Associations between Statin Use and Glaucoma in the All of Us Research Program.

PURPOSE: To investigate associations between statin use and glaucoma in the 2017-2022 All of Us (AoU) Research Program. DESIGN: Cross-sectional, population-based. PARTICIPANTS: 79,742 adult participants aged ≥ 40 years with hyperlipidemia and with electronic health record (EHR) data in the AoU database. METHODS: Hyperlipidemia, glaucoma status, and statin use were defined by diagnoses and medication information in EHR data collected by AoU. Logistic regression analysis was performed to evaluate the association between statin use and glaucoma likelihood. Logistic regression modeling was used to examine associations between glaucoma and all covariates included in adjusted analysis. Serum low-density lipoprotein cholesterol (LDL-C) was used to assess hyperlipidemia severity. Analyses stratified by LDL-C level and age were performed. MAIN OUTCOME MEASURES: Any glaucoma as defined by International Classification of Diseases (ICD) codes found in EHR data. RESULTS: Of 79,742 individuals with hyperlipidemia in AoU, there were 6,365 (8.0%) statin users. Statin use was associated with increased glaucoma prevalence when compared with statin non-use (adjusted odds ratio [aOR]: 1.13, 95% confidence interval [CI]: 1.01-1.26). Higher serum levels of LDL-C were associated with increased odds of glaucoma (aOR: 1.003, 95% CI: 1.003, 1.004). Statin users had significantly higher LDL-C levels compared to nonusers (144.9 mg/dL versus 136.3 mg/dL, p-value < 0.001). Analysis stratified by LDL-C identified positive associations between statin use and prevalence of glaucoma among those with optimal (aOR = 1.39, 95% CI = 1.05-1.82) and high (aOR = 1.37, 95% CI = 1.09-1.70) LDL-C levels. Age-stratified analysis showed a positive association between statin use and prevalence of glaucoma in individuals aged 60-69 years (aOR = 1.28, 95% CI = 1.05-1.56). CONCLUSIONS: Statin use was associated with increased glaucoma likelihood in the overall adult AoU population with hyperlipidemia, in individuals with optimal or high LDL-C levels, and in individuals 60-69 years old. Findings suggest that statin use may be an independent risk factor for glaucoma, which may furthermore be affected by one's lipid profile and age.

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. OBJECTIVE: The study aimed to compare how different model metrics rank the importance of various covariates. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. METHODS: This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. RESULTS: The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). CONCLUSION: The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.

Rheumatoid arthritis and changes on spirometry by smoking status in two prospective longitudinal cohorts.

OBJECTIVE: To compare longitudinal changes in spirometric measures between patients with rheumatoid arthritis (RA) and non-RA comparators. METHODS: We analysed longitudinal data from two prospective cohorts: the UK Biobank and COPDGene. Spirometry was conducted at baseline and a second visit after 5-7 years. RA was identified based on self-report and disease-modifying antirheumatic drug use; non-RA comparators reported neither. The primary outcomes were annual changes in the per cent-predicted forced expiratory volume in 1 s (FEV1%) and per cent predicted forced vital capacity (FVC%). Statistical comparisons were performed using multivariable linear regression. The analysis was stratified based on baseline smoking status and the presence of obstructive pattern (FEV1/FVC <0.7). RESULTS: Among participants who underwent baseline and follow-up spirometry, we identified 233 patients with RA and 37 735 non-RA comparators. Among never-smoking participants without an obstructive pattern, RA was significantly associated with more FEV1% decline (ß=-0.49, p=0.04). However, in ever smokers with ≥10 pack-years, those with RA exhibited significantly less FEV1% decline than non-RA comparators (ß=0.50, p=0.02). This difference was more pronounced among those with an obstructive pattern at baseline (ß=1.12, p=0.01). Results were similar for FEV1/FVC decline. No difference was observed in the annual FVC% change in RA versus non-RA. CONCLUSIONS: Smokers with RA, especially those with baseline obstructive spirometric patterns, experienced lower FEV1% and FEV1/FVC decline than non-RA comparators. Conversely, never smokers with RA had more FEV1% decline than non-RA comparators. Future studies should investigate potential treatments and the pathogenesis of obstructive lung diseases in smokers with RA.

Effects of Blast-and Impact-Related Concussion on Persistent Sleep Problems.

OBJECTIVE: Examine whether concussion mechanism of injury (high-level blast [HLB] vs impact) affects the likelihood of persistent sleep problems in a post-deployment military population. SETTING: Post-Deployment Health Assessment and Re-Assessment survey records completed upon return from deployment and approximately 6 months later. PARTICIPANTS: Active duty enlisted US Marines who completed both assessments (N = 64 464). DESIGN: This retrospective cohort study investigated US Marines deployed between 2008 and 2012. Logistic regression was used to examine persistent sleep problems 6 months after return from deployment. MAIN MEASURES: Self-reported sleep problems at reassessment were investigated as the outcome. Predictors included HLB-induced concussions (mbTBI vs none), impact-induced concussions (miTBI vs none), occupational risk of low-level blast, probable posttraumatic stress disorder (PTSD), depression, alcohol misuse, sleep problems upon deployment return, and relevant interactions, adjusting for sex and pay grade. RESULTS: With the exception of sex, all main effects in the model were associated with greater likelihood of reporting persistent sleep problems at reassessment. Sleep problems at return from deployment showed the strongest associations with likelihood of reporting sleep problems at reassessment, followed by mbTBI. The latter was exacerbated by PTSD and depression. CONCLUSION: mbTBI (vs miTBI) may be more strongly associated with persistent sleep issues that warrant additional monitoring and treatment, particularly among those with probable PTSD and/or depression.

Genome of tropical bed bug Cimex hemipterus (Cimicidae, Hemiptera) reveals tetraspanin expanded in bed bug ancestor.

Cimex species are ectoparasites that exclusively feed on warm-blooded animals such as birds and mammals. Three cimicid species are known to be persistent pests for humans, including the tropical bed bug Cimex hemipterus, common bed bug Cimex lectularius, and Eastern bat bug Leptocimex boueti. To date, genomic information is restricted to the common bed bug C. lectularius, which limits understanding their biology and to provide controls of bed bug infestations. Here, a chromosomal-level genome assembly of C. hemipterus (495 Mb [megabase pairs]) contained on 16 pseudochromosomes (scaffold N50 = 34 Mb), together with 9 messenger RNA and small RNA transcriptomes were obtained. In comparison between hemipteran genomes, we found that the tetraspanin superfamily was expanded in the Cimex ancestor. This study provides the first genome assembly for the tropical bed bug C. hemipterus, and offers an unprecedented opportunity to address questions relating to bed bug infestations, as well as genomic evolution to hemipterans more widely.

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.

OBJECTIVE AND AIMS: Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. METHODS: Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017-2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis. RESULTS: There were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034). CONCLUSION: In conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

Clinical and molecular characteristics of hemophilia A affected individuals and carriers: A 24 years experience from three centers.

Hemophilia A is a rare bleeding disorder with variable expressivity and allelic heterogeneity. Despite the advancement of prenatal diagnostics and molecular studies, the number of studies reviewing the reproductive choices of hemophilia A carriers and affected individuals remains limited. Through this retrospective review, we hope to gain a deeper understanding of hemophilia A-affected individuals' clinical and molecular characteristics, as well as the reproductive choices of the at-risk couples. A total of 122 individuals harboring likely causative F8 gene alterations from 64 apparently unrelated families attending three centers between 3/2000 and 3/2023 were included in this study. Their clinical and molecular findings as well as reproductive choices were gathered in a clinical setting and verified through the electronic medical record database of the public health system. Forty-seven affected males and 75 female heterozygous carriers were included in the analysis. Among 64 apparently unrelated families, 36 distinct pathogenic/likely pathogenic variants were identified, of which 30.6% (11/36) of variants were novel. While the majority of clinical findings and genotype-phenotype correlations appear to be in accordance with existing literature, female carriers who had no fertility intention were significantly more likely to have affected sons than those who had fertility intention (5/19 vs. 4/5; p = 0.047). Through this retrospective review, we summarized the clinical and molecular characteristics of 122 individuals harboring pathogenic/likely pathogenic F8 variants, as well as their fertility intentions and reproductive outcomes. Further studies are required to look into the considerations involved in reproductive decision-making.

Preacinar Arterial Dilation Mediates Outcomes of Quantitative Interstitial Abnormalities in COPDGene.

RATIONALE: Quantitative interstitial abnormalities (QIA) are a computed tomography (CT) measure of early parenchymal lung disease associated with worse clinical outcomes including exercise capacity and symptoms. The presence of pulmonary vasculopathy in QIA and its role in the QIA-outcome relationship is unknown. OBJECTIVES: To quantify radiographic pulmonary vasculopathy in quantitative interstitial abnormalities (QIA) and determine if this vasculopathy mediates the QIA-outcome relationship. METHODS: Ever-smokers with QIA, outcome, and pulmonary vascular mediator data were identified from the COPDGene cohort. CT-based vascular mediators were: right ventricle-to-left ventricle ratio (RV/LV), pulmonary artery-to-aorta ratio (PA/Ao), and pre-acinar intraparenchymal arterial dilation (PA volume 5-20mm2 in cross-sectional area, normalized to total arterial volume). Outcomes were: six-minute walk distance (6MWD) and modified Medical Council Research Council (mMRC) Dyspnea score ≥2. Adjusted causal mediation analyses were used to determine if the pulmonary vasculature mediated the QIA effect on outcomes. Associations of pre-acinar arterial dilation with select plasma biomarkers of pulmonary vascular dysfunction were examined. MAIN RESULTS: Among 8,200 participants, QIA burden correlated positively with vascular damage measures including pre-acinar arterial dilation. Pre-acinar arterial dilation mediated 79.6% of the detrimental impact of QIA on 6MWD (56.2-100%, p<0.001). PA/Ao was a weak mediator and RV/LV was a suppressor. Similar results were observed in the QIA-mMRC relationship. Pre-acinar arterial dilation correlated with increased pulmonary vascular dysfunction biomarker levels including angiopoietin-2 and NT-proBNP. CONCLUSIONS: Parenchymal quantitative interstitial abnormalities (QIA) deleteriously impact outcomes primarily through pulmonary vasculopathy. Pre-acinar arterial dilation may be a novel marker of pulmonary vasculopathy in QIA.

Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking.

Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017. QIA progression was assessed between baseline (visit 1) and 5-year follow-up (visit 2) chest CT scans. Episodes of ARD were defined as increased cough or dyspnea lasting 48 hours and requiring antibiotics or corticosteroids, whereas severe ARD episodes were those requiring an emergency room visit or hospitalization. Episodes were recorded via questionnaires completed every 3 to 6 months. Multivariable logistic regression and zero-inflated negative binomial regression models adjusted for comorbidities (eg, emphysema, small airway disease) were used to assess the association between QIA progression and episodes between visits 1 and 2 (intercurrent) and after visit 2 (subsequent). Results A total of 3972 participants (mean age at baseline, 60.7 years ± 8.6 [SD]; 2120 [53.4%] women) were included. Annual percentage QIA progression was associated with increased odds of one or more intercurrent (odds ratio [OR] = 1.29 [95% CI: 1.06, 1.56]; P = .01) and subsequent (OR = 1.26 [95% CI: 1.05, 1.52]; P = .02) severe ARD events. Participants in the highest quartile of QIA progression (≥1.2%) had more frequent intercurrent ARD (incidence rate ratio [IRR] = 1.46 [95% CI: 1.14, 1.86]; P = .003) and severe ARD (IRR = 1.79 [95% CI: 1.18, 2.73]; P = .006) events than those in the lowest quartile (≤-1.7%). Conclusion QIA progression was independently associated with higher odds of severe ARD events during and after radiographic progression, with higher frequency of intercurrent severe events in those with faster progression. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Little in this issue.

Selective activation of prodrugs in breast cancer using metabolic glycoengineering and the tetrazine ligation bioorthogonal reaction.

Increasing the selectivity of chemotherapies by converting them into prodrugs that can be activated at the tumour site decreases their side effects and allows discrimination between cancerous and non-cancerous cells. Herein, the use of metabolic glycoengineering (MGE) to selectively label MCF-7 breast cancer cells with tetrazine (Tz) activators for subsequent activation of prodrugs containing the trans-cyclooctene (TCO) moiety by a bioorthogonal reaction is demonstrated. Three novel Tz-modified monosaccharides, Ac4ManNTz 7, Ac4GalNTz 8, and Ac4SiaTz 16, were used for expression of the Tz activator within sialic-acid rich breast cancer cells' surface glycans through MGE. Tz expression on breast cancer cells (MCF-7) was evaluated versus the non-cancerous L929 fibroblasts showing a concentration-dependant effect and excellent selectivity with ≥35-fold Tz expression on the MCF-7 cells versus the non-cancerous L929 fibroblasts. Next, a novel TCO-N-mustard prodrug and a TCO-doxorubicin prodrug were analyzed in vitro on the Tz-bioengineered cells to probe our hypothesis that these could be activated via a bioorthogonal reaction. Selective prodrug activation and restoration of cytotoxicity were demonstrated for the MCF-7 breast cancer cells versus the non-cancerous L929 cells. Restoration of the parent drug's cytotoxicity was shown to be dependent on the level of Tz expression where the Ac4ManNTz 7 and Ac4GalNTz 8 derivatives (20 µM) lead to the highest Tz expression and full restoration of the parent drug's cytotoxicity. This work suggests the feasibility of combining MGE and tetrazine ligation for selective prodrug activation in breast cancer.

Proteomics and Machine Learning in the Prediction and Explanation of Low Pectoralis Muscle Area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified 8 biomarkers associated with low pectoralis muscle area (PMA). We built 3 random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified 2 distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Cost effectiveness of nonpharmacological prevention programs for diabetes: A systematic review of trial-based studies.

Trial-based economic value of prevention programs for diabetes is inexplicit. We aimed to review the cost-effectiveness of nonpharmacological interventions to prevent type-2 diabetes mellitus (T2DM) for high-risk people. Six electronic databases were searched up to March 2022. Studies assessing both the cost and health outcomes of nonpharmacological interventions for people at high-risk of T2DM were included. The quality of the study was assessed by the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist. The primary outcome for synthesis was incremental cost-effectiveness ratios (ICER) for quality-adjusted life years (QALYs), and costs were standardized in 2022 US dollars. Narrative synthesis was performed, considering different types and delivery methods of interventions. Sixteen studies included five based on the US diabetes prevention program (DPP), six on non-DPP-based lifestyle interventions, four on health education, and one on screening plus lifestyle intervention. Compared with usual care, lifestyle interventions showed higher potential of cost-effectiveness than educational interventions. Among lifestyle interventions, DPP-based programs were less cost-effective (median of ICERs: $27,077/QALY) than non-DPP-based programs (median of ICERs: $1395/QALY) from healthcare perspectives, but with larger decreases in diabetes incidence. Besides, the cost-effectiveness of interventions was more possibly realized through the combination of different delivery methods. Different interventions to prevent T2DM in high-risk populations are both cost-effective and feasible in various settings. Nevertheless, economic evidence from low- and middle-income countries is still lacking, and interventions delivered by trained laypersons and combined with peer support sessions or mobile technologies could be potentially a cost-effective solution in such settings with limited resources.

Safety, Tolerability, and Dose-Limiting Toxicity of Lacosamide in Patients With Painful Chronic Pancreatitis: Protocol for a Phase 1 Clinical Trial to Determine Safety and Identify Side Effects.

BACKGROUND: Chronic abdominal pain is the hallmark symptom of chronic pancreatitis (CP), with 50% to 80% of patients seeking medical attention for pain control. Although several management options are available, outcomes are often disappointing, and opioids remain a mainstay of therapy. Opioid-induced hyperalgesia is a phenomenon resulting in dose escalation, which may occur partly because of the effects of opioids on voltage-gated sodium channels associated with pain. Preclinical observations demonstrate that the combination of an opioid and the antiseizure drug lacosamide diminishes opioid-induced hyperalgesia and improves pain control. OBJECTIVE: In this phase 1 trial, we aim to determine the safety, tolerability, and dose-limiting toxicity of adding lacosamide to opioids for the treatment of painful CP and assess the feasibility of performance of a pilot study of adding lacosamide to opioid therapy in patients with CP. As an exploratory aim, we will assess the efficacy of adding lacosamide to opioid therapy in patients with painful CP. METHODS: Using the Bayesian optimal interval design, we will conduct a dose-escalation trial of adding lacosamide to opioid therapy in patients with painful CP enrolled in cohorts of size 3. The initial dose will be 50 mg taken orally twice a day, followed by incremental increases to a maximum dose of 400 mg/day, with lacosamide administered for 7 days at each dose level. Adverse events will be documented according to Common Terminology Criteria for Adverse Events (version 5.0). RESULTS: As of December 2023, we have currently enrolled 6 participants. The minimum number of participants to be enrolled is 12 with a maximum of 24. We expect to publish the results by March 2025. CONCLUSIONS: This trial will test the feasibility of the study design and provide reassurance regarding the tolerability and safety of opioids in treating painful CP. It is anticipated that lacosamide will prove to be safe and well tolerated, supporting a subsequent phase 2 trial assessing the efficacy of lacosamide+opioid therapy in patients with painful CP, and that lacosamide combined with opiates will lower the opioid dose necessary for pain relief and improve the safety profile of opioid use in treating painful CP. TRIAL REGISTRATION: Clinicaltrials.gov NCT05603702; https://clinicaltrials.gov/study/NCT05603702. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50513.

Comparative analysis of symptom profile and risk of death associated with infection by SARS-CoV-2 and its variants in Hong Kong.

The recurrent multiwave nature of coronavirus disease 2019 (COVID-19) necessitates updating its symptomatology. We characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. With the COVID-19 cases reported up to August 25, 2022 in Hong Kong, an iterative multitier text-matching algorithm was developed to identify symptoms from free text. Multivariate regression was used to measure associations between variants, symptom development, death, and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Overall, 70.9% (54 450/76 762) of cases were symptomatic with 102 symptoms identified. Intrinsically, the wild-type and delta variant caused similar symptoms among unvaccinated symptomatic cases, whereas the wild-type and omicron BA.2 subvariant had heterogeneous patterns, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting, and sore throat) more frequent in the BA.2 cohort. With ≥2 vaccine doses, BA.2 was more likely than delta to cause fever among symptomatic cases. Fever, blocked nose, pneumonia, and shortness of breath remained jointly predictive of death among unvaccinated symptomatic elderly in the wild-type-to-omicron transition. Number of vaccine doses required for reducing occurrence varied by symptoms. We substantiate that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom-based case identification, increased weighing on symptoms predictive of death in outcome prediction, individual-based risk assessment in care homes, and incorporating free-text symptom reporting.

Adverse jaw outcomes from immune checkpoint inhibitors for head-and-neck cancer? Case reports.

Radiation treatment plays a mainstream role in the management of head and neck cancers (HNSCC). Adverse effects from radiation therapy include osteoradionecrosis of the jaw, and rarely, pathological fracture. Immune checkpoint inhibitors (ICI) such as pembrolizumab are of growing relevance to the management of metastatic and recurrent HNSCC. Adverse impact on bone secondary to medications such as pembrolizumab and nivolumab have been sporadically documented in the literature. The objective of this manuscript is to raise awareness of possible increase in risk for adverse jaw outcomes in patients with HNSCC exposed to both radiation treatment to the jaws and ICI therapy. This manuscript documents adverse jaw outcomes including osteonecrosis and pathological fracture of the mandible in two patients receiving pembrolizumab for management of HNSCC and had received prior radiation treatment. A potential link between immunotherapy and adverse jaw outcomes is consistent with our growing understanding of osteoimmunology, investigating the closely interrelated processes in bone remodeling and immune system function, in health and disease. It is important to ascertain if pembrolizumab poses an incremental risk for such outcomes, beyond the risk from prior radiation, for patients managed with radiation treatment and ICI therapy for HNSCC. The general dentist may encounter such patients either in the context of facilitating dental clearance prior to initiation of chemotherapy, or rarely, with poorly explained jaw symptoms and must be alert to the possibility of occurrence of such adverse jaw events to facilitate timely diagnosis and optimal patient management.

A Review on Endoscopic Management of Acute Cholecystitis: Endoscopic Ultrasound-Guided Gallbladder Drainage and Endoscopic Transpapillary Gallbladder Drainage.

A percutaneous cholecystostomy tube (PCT) is the conventionally favored nonoperative intervention for treating acute cholecystitis. However, PCT is beset by high adverse event rates, need for scheduled reintervention, and inadvertent dislodgement, as well as patient dissatisfaction with a percutaneous drain. Recent advances in endoscopic therapy involve the implementation of endoscopic transpapillary drainage (ETP-GBD) and endoscopic ultrasound-guided gallbladder drainage (EUS-GBD), which are increasingly preferred over PCT due to their favorable technical and clinical success combined with lower complication rates. In this article, we provide a comprehensive review of the literature on EUS-GBD and ETP-GBD, delineating instances when clinicians should opt for endoscopic management and highlighting potential risks associated with each approach.

Cognitive dispersion is related to subtle objective daily functioning changes in older adults with and without cognitive impairment.

Early detection of cognitive and functional decline is difficult given that current tools are insensitive to subtle changes. The present study evaluated whether cognitive dispersion on neuropsychological testing improved prediction of objectively assessed daily functioning using unobtrusive monitoring technologies. Hierarchical linear regression was used to evaluate whether cognitive dispersion added incremental information beyond mean neuropsychological performance in the prediction of objectively assessed IADLs (i.e., computer use, pillbox use, driving) in a sample of 104 community-dwelling older adults without dementia (Mage = 74.59, 38.5% Female, 90.4% White). Adjusting for age, sex, education, and mean global cognitive performance, cognitive dispersion improved prediction of average daily computer use duration (R2 Δ = 0.100, F Change, p = 0.005), computer use duration variability (R2 Δ = 0.089, F Change p = 0.009), and average daily duration of nighttime driving (R2 Δ = 0.072, F Change p = 0.013). These results suggest cognitive dispersion may improve prediction of objectively assessed functional changes in older adults without dementia.

Age-dependent association of central blood pressure with cardiovascular outcomes: a cohort study involving 34 289 participants using the UK biobank.

BACKGROUND: It remained unclear whether central blood pressures (BP) was more closely associated with cardiovascular disease (CVD) than brachial BP in different age groups. OBJECTIVES: To investigate the age-stratified association of CVD with brachial and central BPs, and to evaluate corresponding improvement in model performance. METHODS: This cohort study included 34 289 adults without baseline CVD from the UK Biobank dataset. Participants were categorized into middle-aged and older aged groups using the cut-off of age 65 years. The primary endpoint was a composite cardiovascular outcome consisting of cardiovascular mortality combined with nonfatal coronary events, heart failure and stroke. Multivariable-adjusted hazard ratios expressed CVD risks associated with BP increments of 10 mmHg. Akaike Information Criteria (AIC) was used for model comparisons. RESULTS: In both groups, CVD events were associated with brachial or central SBP ( P  ≤ 0.002). Model fit was better for central SBP in middle-aged adults (AIC 4427.2 vs. 4429.5), but model fit was better for brachial SBP in older adults (AIC 10 246.7 vs. 10 247.1). Central SBP remained significantly associated to CVD events [hazard ratio = 1.05; 95% confidence interval (CI) 1.0-1.1] and improved model fit (AIC = 4426.6) after adjustment of brachial SBP only in the middle-aged adults. These results were consistent for pulse pressure (PP). CONCLUSION: In middle-aged adults, higher central BPs were associated with greater risks of CVD events, even after adjusting for brachial BP indexes. For older adults, the superiority of central BP was not observed. Additional trials with adequate follow-up time will confirm the role of central BP in estimating CVD risk for middle-aged individuals.