Assessment of nebivolol efficacy in experimental models of toxoplasmosis: insights into parasite burden reduction and neuronal protection.

This study investigates the efficacy of nebivolol (NBV) in experimental models of toxoplasmosis, focusing on parasite burden reduction and neuronal protection. In the acute model of experimental toxoplasmosis, Swiss mice infected with RH strain tachyzoites received oral NBV chlorhydrate doses of 2 mg/kg/day and 4 mg/kg/day for 8 days. Treatment with NBV significantly reduced parasite burden compared to vehicle and standard drug (PYR) groups. In the chronic model of experimental toxoplasmosis, C57/BL6 mice infected with the ME49 strain received NBV chlorhydrate 41 days post-infection and were evaluated after 10 days of treatment. NBV chlorhydrate effectively reduced cyst number and area, as well as bradyzoite burden compared to controls. Histological analysis demonstrated that NBV chlorhydrate preserved neuronal count, with the 4 mg/kg/day dose yielding counts similar to non-infected mice. Statistical analysis confirmed significant differences compared to control groups. Furthermore, immunohistochemical analysis revealed a significant reduction in iNOS labeling in the brains of mice treated with NBV chlorhydrate, indicating a decrease in nitric oxide production compared to control groups. These findings suggest NBV's potential as a promising candidate for toxoplasmosis treatment, highlighting its ability to reduce parasite burden and protect neuronal integrity. Further research is warranted to elucidate NBV's mechanisms of action and its clinical application in managing toxoplasmosis.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer.

BACKGROUND: Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. METHODS: Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense's regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from 'non-COPD' (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. RESULTS: The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. CONCLUSION: The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. TRIAL REGISTRATION: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288.

WHO guidance on mental health training: a systematic review of the progress for non-specialist health workers.

OBJECTIVE: To assess existing literature on the effectiveness of mental health training courses for non-specialist health workers, based on the WHO guidelines (2008). DESIGN: A systematic review was carried out, complying with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. DATA SOURCES: After examination of key studies in the literature, a comprehensive search was performed within the following electronic databases on 31 May 2017: PubMed, PsycINFO, CINAHL (using EBSCOHost interface), Cochrane, Web of Science. ELIGIBILITY CRITERIA: Searches were conducted for articles published in English from January 2008 to May 2017, using search terms related to mental health, training, community care and evaluation/outcome, following the Participants, Interventions, Comparators and Outcomes process for evidence-based practice. OUTCOMES: Data were collected across the following categories: trainees (number and background), training course (curriculum, teaching method, length), evaluation method (timing of evaluation, collection method and measures assessed) and evaluation outcome (any improvement recorded from baseline). In addition, studies were assessed for their methodological quality using the framework established by Liu et al (2016). RESULTS: 29 studies with relevant training courses met the inclusion criteria. These were implemented across 16 countries since 2008 (over half between 2014 and 2017), with 10 in three high-income countries. Evaluation methods and outcomes showed high variability across studies, with courses assessing trainees' attitude, knowledge, clinical practice, skills, confidence, satisfaction and/or patient outcome. All 29 studies found some improvement after training in at least one area, and 10 studies found this improvement to be significant. CONCLUSIONS: Training non-specialist workers in mental healthcare is an effective strategy to increase global provision and capacity, and improves knowledge, attitude, skill and confidence among health workers, as well as clinical practice and patient outcome. Areas for future focus include the development of standardised evaluation methods and outcomes to allow cross-comparison between studies, and optimisation of course structure. PROSPERO REGISTRATION NUMBER: CRD42016033269.

Public health interventions to protect against falsified medicines: a systematic review of international, national and local policies.

BACKGROUND: Falsified medicines are deliberately fraudulent drugs that pose a direct risk to patient health and undermine healthcare systems, causing global morbidity and mortality. OBJECTIVE: To produce an overview of anti-falsifying public health interventions deployed at international, national and local scales in low and middle income countries (LMIC). DATA SOURCES: We conducted a systematic search of the PubMed, Web of Science, Embase and Cochrane Central Register of Controlled Trials databases for healthcare or pharmaceutical policies relevant to reducing the burden of falsified medicines in LMIC. RESULTS: Our initial search identified 660 unique studies, of which 203 met title/abstract inclusion criteria and were categorised according to their primary focus: international; national; local pharmacy; internet pharmacy; drug analysis tools. Eighty-four were included in the qualitative synthesis, along with 108 articles and website links retrieved through secondary searches. DISCUSSION: On the international stage, we discuss the need for accessible pharmacovigilance (PV) global reporting systems, international leadership and funding incorporating multiple stakeholders (healthcare, pharmaceutical, law enforcement) and multilateral trade agreements that emphasise public health. On the national level, we explore the importance of establishing adequate medicine regulatory authorities and PV capacity, with drug screening along the supply chain. This requires interdepartmental coordination, drug certification and criminal justice legislation and enforcement that recognise the severity of medicine falsification. Local healthcare professionals can receive training on medicine quality assessments, drug registration and pharmacological testing equipment. Finally, we discuss novel technologies for drug analysis which allow rapid identification of fake medicines in low-resource settings. Innovative point-of-purchase systems like mobile phone verification allow consumers to check the authenticity of their medicines. CONCLUSIONS: Combining anti-falsifying strategies targeting different levels of the pharmaceutical supply chain provides multiple barriers of protection from falsified medicines. This requires the political will to drive policy implementation; otherwise, people around the world remain at risk.

Quantifying postural tremor in workers exposed to low levels of manganese.

The aim of this study was: (1) To determine the minimum number of characteristics necessary to discriminate between postural tremor recorded in control subjects (CO), in subjects exposed to manganese (MN), and in patients with Parkinson's disease (PD), and (2) to examine the continuum of changes between the three groups examined. Workers previously exposed to Mn (n = 10), patients with PD (n = 10), and control subjects (CO) (n = 11) underwent a clinical examination. Blood Mn was measured at the end of exposure time for the MN group and 12 months later at the beginning of the experiment for all groups. Postural tremor with visual feedback was recorded in the index finger with a laser system. Statistical criteria were used to reduce computed tremor characteristics to a minimal set of reliable discriminating variables. Two variables were retained namely corrected wobble (CW), describing the morphology of the tremor oscillations, and variability ratio (VR), describing proportional power of tremor. Both variables had an overall correct classification rate of 77.4%. Blood Mn levels at the time of the experiment were similar for all groups and had insignificant correlation with tremor variables. However, blood Mn levels in workers which were also measured at the end of exposure time (i.e., 12 months before) showed significant correlation (Spearman's rank coefficient) with both harmonic index (rho = 0.70, P = 0.03) and first maximum of the autocorrelation function (rho = 0.89, P = 0.001). We conclude that (1) the tremor of workers exposed to Mn could be adequately described with only two variables; (2) a continuum of changes between tremor recorded in control subjects, in subjects exposed to Mn and in patients with PD was observed, with the MN group always found in between the control (CO) and the PD groups; (3) while blood Mn levels in workers were back at control levels at the time of the experiment, the effect of Mn on postural tremor was still detected. Thus our method has the potential to detect the effect of Mn on tremor with only two variables even after Mn level in the blood is back to normal values.