BackgroundCurrent depression, anxiety, and suicide screening techniques rely on retrospective patient reported symptoms to standardized scales. A qualitative approach to screening combined with the innovation of natural language processing (NLP) and machine learning (ML) methods have shown promise to enhance person-centeredness while detecting depression, anxiety, and suicide risk from in-the-moment patient language derived from an open-ended brief interview.ObjectiveTo evaluate the performance of NLP/ML models to identify depression, anxiety, and suicide risk from a single 5–10-min semi-structured interview with a large, national sample.MethodTwo thousand four hundred sixteen interviews were conducted with 1,433 participants over a teleconference platform, with 861 (35.6%), 863 (35.7%), and 838 (34.7%) sessions screening positive for depression, anxiety, and suicide risk, respectively. Participants completed an interview over a teleconference platform to collect language about the participants’ feelings and emotional state. Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB) models were trained for each condition using term frequency-inverse document frequency features from the participants’ language. Models were primarily evaluated with the area under the receiver operating characteristic curve (AUC).ResultsThe best discriminative ability was found when identifying depression with an SVM model (AUC = 0.77; 95% CI = 0.75–0.79), followed by anxiety with an LR model (AUC = 0.74; 95% CI = 0.72–0.76), and an SVM for suicide risk (AUC = 0.70; 95% CI = 0.68–0.72). Model performance was generally best with more severe depression, anxiety, or suicide risk. Performance improved when individuals with lifetime but no suicide risk in the past 3 months were considered controls.ConclusionIt is feasible to use a virtual platform to simultaneously screen for depression, anxiety, and suicide risk using a 5-to-10-min interview. The NLP/ML models performed with good discrimination in the identification of depression, anxiety, and suicide risk. Although the utility of suicide risk classification in clinical settings is still undetermined and suicide risk classification had the lowest performance, the result taken together with the qualitative responses from the interview can better inform clinical decision-making by providing additional drivers associated with suicide risk.

Background: The purpose of the study trial was to assess therapeutic efficacy of diminazene aceturate and artesunate with respect to clinical, haematological and biochemical changes in the cattle affected by babesiosis. Methods: For the analysis, a total of 16 clinically affected cattle were selected, with eight animals in each group. Eight healthy cattle were also selected under the control group. Clinical symptoms, blood smear microscopy and PCR-based molecular tools were used to confirm babesiosis. For the therapeutic trial, Groups I and II received treatment with diminazene aceturate and artesunate, respectively and the efficacy was estimated on the basis of survival rate, and improvement in the clinico-haemato-biochemical parameters. Result: The major clinical signs recorded were persistent high fever, pale mucous membranes, presence of ticks, decreased ruminal motility and haemoglobinuria. In diseased cattle (n=16) Hb, PCV, and TEC levels were significantly (P less than 0.05) low but with a significant (P less than 0.05) increase in TLC as compared to the control group (n=8). Significantly (P less than 0.05) neutrophilia, lymphopenia, hypoglycaemia, hypoproteinemia, hypoalbuminaemia, was recorded in the diseased group. Moreover, significantly (P less than 0.05) increased levels of BUN, creatinine, AST, LDH and iron were recorded in the infected animals. Group I treated with diminazene aceturate was the most effective and cost- efficient treatment for bovine babesiosis compared to Group II treated with Artesunate.

Psychiatry is a specialized branch of medicine that studies behavioural disorders in humans and animals. In dogs and cats, behavioural problems are analogous to human psychiatric diseases, with the most common psychopathologies addressed being generalized anxiety disorder, obsessive compulsive disorder, separation anxiety and post-traumatic stress disorder. The level of bizarre behaviours displayed by the pets varies due to a variety of factors such as hereditary, prenatal dam modification and events during their neonatal and socializing phase. The behavioural changes recorded in dogs and cats include trembling, vocalizing, urinating, soiling, defecating, salivating, hiding, destructiveness, aggression, fear and obsessive behaviours. It is critical to remember that other medical complications, that can produce similar behavioural changes, must be addressed for the animal’s specific therapy. For the treatment of behavioural disorders in pets, different therapeutic agents (like Tricyclic antidepressants, selective serotonin reuptake inhibitors, benzodiazepines and a typical antidepressants) are used together with the behavioural modification strategies.