Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults.

Importance: Understanding the differences and potential synergies between traditional clinician assessment and automated machine learning might enable more accurate and useful suicide risk detection. Objective: To evaluate the respective and combined abilities of a real-time machine learning model and the Columbia Suicide Severity Rating Scale (C-SSRS) to predict suicide attempt (SA) and suicidal ideation (SI). Design, Setting, and Participants: This cohort study included encounters with adult patients (aged ≥18 years) at a major academic medical center. The C-SSRS was administered during routine care, and a Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) prediction was generated in the electronic health record. Encounters took place in the inpatient, ambulatory surgical, and emergency department settings. Data were collected from June 2019 to September 2020. Main Outcomes and Measures: Primary outcomes were the incidence of SA and SI, encoded as International Classification of Diseases codes, occurring within various time periods after an index visit. We evaluated the retrospective validity of the C-SSRS, VSAIL, and ensemble models combining both. Discrimination metrics included area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPR), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The cohort included 120 398 unique index visits for 83 394 patients (mean [SD] age, 51.2 [20.6] years; 38 107 [46%] men; 45 273 [54%] women; 13 644 [16%] Black; 63 869 [77%] White). Within 30 days of an index visit, the combined models had higher AUROC (SA: 0.874-0.887; SI: 0.869-0.879) than both the VSAIL (SA: 0.729; SI: 0.773) and C-SSRS (SA: 0.823; SI: 0.777) models. In the highest risk-decile, ensemble methods had PPV of 1.3% to 1.4% for SA and 8.3% to 8.7% for SI and sensitivity of 77.6% to 79.5% for SA and 67.4% to 70.1% for SI, outperforming VSAIL (PPV for SA: 0.4%; PPV for SI: 3.9%; sensitivity for SA: 28.8%; sensitivity for SI: 35.1%) and C-SSRS (PPV for SA: 0.5%; PPV for SI: 3.5%; sensitivity for SA: 76.6%; sensitivity for SI: 68.8%). Conclusions and Relevance: In this study, suicide risk prediction was optimal when leveraging both in-person screening (for acute measures of risk in patient-reported suicidality) and historical EHR data (for underlying clinical factors that can quantify a patient's passive risk level). To improve suicide risk classification, prediction systems could combine pretrained machine learning with structured clinician assessment without needing to retrain the original model.

A novel method of screening combinations of angiostatics identifies bevacizumab and temsirolimus as synergistic inhibitors of glioma-induced angiogenesis.

Tumor angiogenesis is critical for the growth and progression of cancer. As such, angiostasis is a treatment modality for cancer with potential utility for multiple types of cancer and fewer side effects. However, clinical success of angiostatic monotherapies has been moderate, at best, causing angiostatic treatments to lose their early luster. Previous studies demonstrated compensatory mechanisms that drive tumor vascularization despite the use of angiostatic monotherapies, as well as the potential for combination angiostatic therapies to overcome these compensatory mechanisms. We screened clinically approved angiostatics to identify specific combinations that confer potent inhibition of tumor-induced angiogenesis. We used a novel modification of the ex ovo chick chorioallantoic membrane (CAM) model that combined confocal and automated analyses to quantify tumor angiogenesis induced by glioblastoma tumor onplants. This model is advantageous due to its low cost and moderate throughput capabilities, while maintaining complex in vivo cellular interactions that are difficult to replicate in vitro. After screening multiple combinations, we determined that glioblastoma-induced angiogenesis was significantly reduced using a combination of bevacizumab (Avastin®) and temsirolimus (Torisel®) at doses below those where neither monotherapy demonstrated activity. These preliminary results were verified extensively, with this combination therapy effective even at concentrations further reduced 10-fold with a CI value of 2.42E-5, demonstrating high levels of synergy. Thus, combining bevacizumab and temsirolimus has great potential to increase the efficacy of angiostatic therapy and lower required dosing for improved clinical success and reduced side effects in glioblastoma patients.

The Pharmacologic Treatment of Stuttering and Its Neuropharmacologic Basis.

Stuttering is a DSM V psychiatric condition for which there are no FDA-approved medications for treatment. A growing body of evidence suggests that dopamine antagonist medications are effective in reducing the severity of stuttering symptoms. Stuttering shares many similarities to Tourette's Syndrome in that both begin in childhood, follow a similar male to female ratio of 4:1, respond to dopamine antagonists, and symptomatically worsen with dopamine agonists. In recent years, advances in the neurophysiology of stuttering have helped further guide pharmacological treatment. A newer medication with a novel mechanism of action, selective D1 antagonism, is currently being investigated in FDA trials for the treatment of stuttering. D1 antagonists possess different side-effect profiles than D2 antagonist medications and may provide a unique option for those who stutter. In addition, VMAT-2 inhibitors alter dopamine transmission in a unique mechanism of action that offers a promising treatment avenue in stuttering. This review seeks to highlight the different treatment options to help guide the practicing clinician in the treatment of stuttering.

Funding Instability Reduces the Impact of the Federal Teaching Health Center Graduate Medical Education Program.

The Teaching Health Center Graduate Medical Education (THCGME) program is a decentralized residency training component of the Affordable Care Act, created to combat critical shortages and maldistribution of primary care physicians. The Accreditation Council of Graduate Medical Education and federal data reveal that the THCGME program accounted for 33% of the net increase in family medicine residency positions between 2011 and 2015. However, amid concerns about the program's stability, the contribution of the THCGME program to the net increase fell to 7% after 2015.

A human FSHB promoter SNP associated with low FSH levels in men impairs LHX3 binding and basal FSHB transcription.

FSH production is important for human gametogenesis. In addition to inactivating mutations in the FSHB gene, which result in infertility in both sexes, a G/T single-nucleotide polymorphism (SNP) at -211 relative to the transcription start site of the 5' untranslated region of FSHB has been reported to be associated with reduced serum FSH levels in men. In this study, we sought to identify the potential mechanism by which the -211 SNP reduces FSH levels. Although the SNP resides in a putative hormone response element, we showed that, unlike the murine gene, human FSHB was not induced by androgens or progestins in gonadotropes. On the other hand, we found that the LHX3 homeodomain transcription factor bound to an 11-bp element in the human FSHB promoter that includes the -211 nucleotide. Furthermore, we also demonstrated that LHX3 bound with greater affinity to the wild-type human FSHB promoter compared with the -211 G/T mutation and that LHX3 binding was more effectively competed with excess wild-type oligonucleotide than with the SNP. Finally, we showed that FSHB transcription was decreased in gonadotrope cells with the -211 G/T mutation compared with the wild-type FSHB promoter. Altogether, our results suggest that decreased serum FSH levels in men with the SNP likely result from reduced LHX3 binding and induction of FSHB transcription.