Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample.

Importance: Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. Objective: To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. Design, Setting, and Participants: This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Main Outcome and Measures: Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. Results: The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. Conclusions and Relevance: In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Application of enzyme preparations for extraction of berry skin phenolics in withered winegrapes.

Postharvest withering of grapes strongly affects the content and extractability of phenolic compounds in the production of sfursat, fortified and passito wines. This work evaluated the effectiveness of enzymes applied individually and/or in multi-enzyme blends, on the extraction of anthocyanins, oligomeric flavanols and polymeric flavanols from withered grape skins during simulated maceration. The study was performed on Vitis vinifera L. cv. Nebbiolo and Barbera because of their different skin phenolic profile and cell wall composition. Our findings highlight that the relationship between skin mechanical properties (berry skin break force and energy) and extraction yield of phenolic compounds is variety dependent. Significant correlations were found between the skin softening associated with cell wall degradation and the extraction of anthocyanins and flavanols in Nebbiolo, for which polygalacturonase, individually or in multi-enzyme blends, plays an important role. In Barbera, the extractability of phenolic compounds was not affected by the presence of exogenous enzymes.

Real time monitoring of alcoholic fermentation with low-cost amperometric biosensors.

Screen-printed glucose, ethanol and fructose biosensors, coupled with portable instrumentation, and their application to monitor micro-alcoholic fermentations (micro-ALFs) in red wine is described. For the fabrication of glucose and ethanol biosensors, graphite screen-printed sensors modified with Prussian Blue were coupled with oxidase enzymes while for the fructose biosensor, a bare screen-printed sensor was coated with fructose dehydrogenase and phenazine methansulphate was used as electrochemical mediator. The working range, reproducibility of probe fabrication and biosensor stability were all evaluated. After a recovery study, performed analysing fortified must-wine samples, the biosensors were employed to monitor micro-ALFs induced by the inoculation of two different strains of Saccharomyces cerevisiae. During the red micro-ALFs, samples of must-wine were collected and analysed by use of both biosensors and spectrophotometric kits. The data obtained demonstrated that a biosensor-based system could represent a useful tool to assist winemakers during wine production.

Development of a biosensor for monitoring of glycerol during alcoholic fermentation.

A biosensor for the measurement of glycerol in FIA was constructed using covalently immobilized glycerokinase and glycerol-3-phosphate oxidase in conjunction with a Pt based hydrogen peroxide probe. Different immobilization strategies have been studied including random and asymmetric immobilization onto a polymeric support and immobilization onto two different membranes. The latter resulted in the best configuration for batch measurement. The most effective configuration for measurement in FIA was the immobilization of glycerokinase in a glass beads reactor coupled with glycerol-3-phosphate oxidase on a preactivated Immobilon AV membrane kept at the electrode surface. Using a 250-microliter injection loop, 3 mmol ATP(Mg+2) in 0.1 M borate buffer pH 8.5 and a flow rate of 0.5 ml/min, a linear response in the 2 x 10(-6)/10(-3) mol/l range and a detection limit of 5 x 10(-7) mol/l were obtained for glycerol. Lifetime of the glycerol-3-phosphate membrane was extended up to 1 month by storage in the working buffer containing 1% DEAE-dextran and 5% lactitol. More than 350 samples can be assayed with this system. The biosensor was used to monitor off-line glycerol production during alcoholic fermentations carried out at different pHs and temperatures.

Phenolic Compounds in Different Olive Varieties.

Phenolic compounds in different olive varieties were determined by HPLC analysis over 2 years. Demethyloleuropein was found in only two (Coratina and Leccino) of the eight varieties studied, so it could be used as varietal marker. Elenolic acid glucoside and hydroxytyrosol can be considered indicators of maturation for olives. In fact, as the olives ripen, their tenor increases whereas oleuropein decreases.

Bioelectrochemical determination of lactic and malic acids in wine.

Electroanalytical methods based on biosensor technology and differential pH measurement for the determination of l-lactic and l-malic acids in wine were developed. Lactic acid was determined according to two procedures consisting of the use of two lactate electrochemical biosensors, one based on a Clark O(2) probe and the other based on the H(2)O(2) probe. The two probes were assembled employing a polymeric membrane where the enzyme lactate oxidase was previously immobilized. Both sensors exhibited high storage and operational stabilities, and good reproducibility when used in wine matrix. Measurement of lactate was carried out using two different flow-through cells assembled with the lactate probes. The low detection limit of these probes and the relatively high concentration of lactate in wine samples required a serial dilution in the range 1:100 to 1:200, thus eliminating all potential electrochemical or enzyme interferences present in the sample. Malic acid was determined using a differential commercially available pH-meter and an enzymatic procedure. For this analysis, we employed the malic enzyme (EC 1.1.1.40) and the NADP(+) cofactor. The pH variation due to the action of this enzyme on malic acid was found to be proportional to the malic acid present in the sample. Experimental parameters such as pH, temperature, and co-factor concentration were optimized, resulting in malate determination in less than 1 min with good reproducibility. Various samples of wine and wine musts were assayed for lactate with the two biosensor procedures, and for malate with differential pH-metry. The results when compared with those obtained with the commonly used spectrophotometric procedure correlated well.