Human papillomavirus (HPV) is a leading cause of cervical cancer globally. High-risk (HR) HPV types HPV16 and 18 are responsible for 70
Abstract Pretomanid is a key component of the bedaquiline, pretomanid, linezolid with or without moxifloxacin (BPaL/M) regimen recommended for treatment of rifampicin-resistant tuberculosis (RR-TB). To support dose optimization and efficacy interpretation, we developed a pretomanid population pharmacokinetic (PK) model and evaluated exposure and probability of target attainment (PTA). Ninety-four RR-TB patients received daily oral pretomanid at 200 mg, and plasma samples were collected at multiple time points. Pretomanid concentrations were quantified using high-performance liquid chromatography-tandem mass spectrometry and PK modeling was performed using nlmixr2 in R. A one-compartment model with first-order absorption and elimination, and fat free mass allometric scaling best described the data. Typical clearance was 3.10 L/h (32.9%CV), median AUC₀₋₂₄ was 64,000 (31,000– 140,000) ng·h/ml, and the median maximum inter-dose concentration (Cmax) was 3,000 (1,000– 6,000) ng/ml. Pretomanid MICs for Mycobacterium tuberculosis in the TB-PRACTECAL trial were consistently below the provisional critical concentration, with a median of 0.125 mg/L. PK-Pharmacodynamic (PD) simulations indicated that nearly all participants achieved drug exposures exceeding %fT > MIC target, consistent with the regimen’s clinical efficacy across the study population. However, the AUC/MIC target was not achieved. We developed a pretomanid population PK model and facilitated exploring robustness of PK-PD targets for PTA. Our study confirmed the clinical relevance of the time dependent index and target, but further investigation is needed to determine whether the 167 AUC/MIC target is valid in patients or using translational pre-clinical experiments, especially within the context of combination therapy. Trial registry: Clinical Trials.gov, TRN: NCT04081077, Registration date: 4 September 2019.
BACKGROUND:In tuberculosis (TB) care, adherence is often assessed using a simple 80% threshold, which may overlook meaningful patterns. We analyzed adherence trajectories among individuals treated for rifampicin- or multidrug-resistant TB (RR/MDR-TB) in the endTB observational study to identify more informative patterns. METHODS:We applied a joint latent class mixed model to classify adherence trajectories and assess their relationship with treatment outcomes. Model performance was compared to common classification methods (eg 80% adherence threshold) using Kendall's τb and area under the receiver operating curve for predicting unsuccessful outcomes. RESULTS:Among 1787 individuals, we identified 4 adherence patterns: "consistently high" (72.5%), "high to low" (14.3%), "low to high" (7.3%), and "consistently low" (5.9%). Compared to the "consistently high" group, those in "high to low" (hazard ratio [HR] = 23.2; 95% confidence interval [CI]: 15.7-24.3) and "consistently low" (HR = 43.2; 95% CI: 26.2-71.5) groups had significantly higher risk of unsuccessful outcomes, while the "low to high" group did not (HR = 0.7; 95% CI: .1-3.8). Our trajectory model more accurately predicted outcomes than common classification methods (P < .01). CONCLUSIONS:Group-based trajectory modeling provides more nuanced insights into adherence patterns than conventional classification methods. Our findings demonstrate that patients with RR/MDR-TB who exhibited initial poor adherence followed by subsequent improvement achieved clinical outcomes comparable to those with consistently high adherence throughout treatment. This finding challenges the prevailing assumption that sustained high adherence is necessary for treatment success, suggesting that adherence patterns, rather than overall adherence rates, may be more predictive of clinical outcomes in the management of RR/MDR-TB.
Aims: Healthcare providers working in conflict zones face unique occupational and psychological challenges that significantly impair sleep quality. In the Gaza Strip, prolonged exposure to violence and humanitarian crises exacerbates these challenges, yet data on the sleep health of this critical workforce remain scarce. This cross-sectional study aimed to assess the prevalence and patterns of sleep disturbances among healthcare providers at Nasser Medical Complex during the 2023–2025 Israel–Gaza conflict, and to examine associations between sleep quality and sociodemographic and occupational factors. Methods: A cross-sectional study was conducted among 400 healthcare providers (70% nurses, 20% physicians, 10% non-medical staff) at Nasser Medical Complex from May to July 2025. Of 1000 eligible, 993 were approached; 400 participated (participation rate 40.3%). Participants completed the validated Arabic version of the Pittsburgh Sleep Quality Index (PSQI) and a sociodemographic questionnaire. Descriptive statistics, bivariate analyses, and multivariate regression were used to evaluate sleep quality and its predictors. Ethical approval: Palestinian Ministry of Health (Ref 2563158). No funding. Results: The PSQI demonstrated acceptable internal consistency (Cronbach’s alpha [value pending]). Thirty-five per cent of participants reported poor sleep quality (PSQI >5). Additionally, 40% reported sleeping less than 6–7 hours nightly. Sleep disturbances were frequent, including difficulty initiating sleep (50% reporting problems weekly or more), nighttime awakenings (60%), loud snoring (37.5%), and breathing pauses (20%). Physical discomfort during sleep–such as back pain and breathing difficulties–was prevalent. Conclusion: Sleep disturbances are alarmingly common among Gaza’s healthcare providers in conflict settings. These findings underscore the urgent need for integrated mental health and sleep interventions, occupational health screenings, and infrastructural support to safeguard provider wellbeing and healthcare delivery sustainability in protracted crises.
