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Female Genital mutilation/cutting (FGM/C) is associated with enduring psychiatric complications. In this study, we investigate the rates of co-morbid abuses and polyvictimization experienced by survivors of FGM/C. This is a sub-analysis of a cohort study examining the patient population at the EMPOWER Center for Survivors of Sex Trafficking and Sexual Violence in New York City. A retrospective chart-review of electronic medical records was conducted for all consenting adult patients who had FGM/C and had an intake visit between January 16, 2014 and March 6, 2020. Of the 80 participants, ages ranged from 20 to 62 years with a mean of 37.4 (SD = 9.1) years. In addition to FGM/C, participants were victims of physical abuse (43; 53.8%), emotional abuse (35; 43.8%), sexual abuse (35; 43.8%), forced marriage (20; 25%), child marriage (13; 16.3%), and sex trafficking (1; 1.4%). There was a high degree of polyvictimization, with 41 (51.2%) experiencing 3 or more of the aforementioned abuses. Having FGM/C on or after age 13 or having a higher total abuse score was also found to be strong predictors of depression and PTSD. The high rates of polyvictimization among survivors of FGM/C are associated with development of depression and PTSD. Despite co-morbid abuses, patients still attribute substantial psychiatric symptoms to their FGM/C. Health care providers should understand the high risk of polyvictimization when caring for this patient population.

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI’s superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Introduction Transgender women are the population most vulnerable to HIV in Latin America, with prevalence between 18 and 38%. Although the region has improved antiretroviral coverage, there is an urgent need to strengthen HIV prevention for key populations to meet regional targets set by governments. We conducted an assessment on the state of HIV prevention among transgender women in Latin America. Methods We conducted a desk review of Global AIDS Response Progress Reports, national strategic plans, technical reports and peer‐reviewed articles from 17 Latin American countries published through January 2015. The review was preceded by 12 semi‐structured interviews with UNAIDS and Pan American Health Organization officers and a discussion group with transgender women regional leaders, to guide the identification of documents. We assessed access to, implementation and coverage of programmes; legal frameworks; community participation; inclusion of new strategies; and alignment with international recommendations. Results and discussion Overall, prevention activities in the region focus on condom distribution, diagnosis of sexually transmitted infections and peer education, mostly delivered at health facilities, with limited community involvement. Argentina and Uruguay have implemented structural interventions to address social inclusion. Argentina, Brazil and Mexico have adopted early initiation of antiretroviral therapy and treatment as prevention strategies. The other countries do not have substantial tailored interventions and consider the trans population a sub‐population of men who have sex with men in data collection and programme implementation. Limited coverage of services, discrimination and a deep‐seated mistrust of the health system among transgender women are the main barriers to accessing HIV prevention services. Promising interventions include health services adapted to transgender women in Mexico; LGBT‐friendly clinics in Argentina that incorporate community and health workers in mixed teams; task‐shifting to community‐based organizations; mobile HIV testing; and gender identity laws. Conclusions Transgender women in Latin America continue to have limited access to HIV prevention services, which presents a bottleneck for reaching prevention goals and incorporating new prevention interventions. Prevention programmes should be rights‐based; offer tailored, holistic interventions; and involve transgender women in their design and implementation.

Some supermarkets have to ask customers to pre-pay for oysters because they've disappeared come checkout time. According to the Stuff website, shoplifting costs New Zealand supermarkets $760 million annually - well costs us honest suckers, actually.

Identifying patient-specific clonal IGH/TCR junctional sequences is critical for minimal residual disease (MRD) monitoring. Conventionally these junctional sequences are identified using laborious Sanger sequencing of excised heteroduplex bands. We found that the IGH is highly expressed in our diagnostic B-cell acute lymphoblastic leukemia (B-ALL) samples using RNA-Seq. Therefore, we used RNA-Seq to identify IGH disease clone sequences in 258 childhood B-ALL samples for MRD monitoring. The amount of background IGH rearrangements uncovered by RNA-Seq followed the Zipf’s law with IGH disease clones easily identified as outliers. Four hundred and ninety-seven IGH disease clones (median 2, range 0–7 clones/patient) are identified in 90.3% of patients. High hyperdiploid patients have the most IGH disease clones (median 3) while DUX4 subtype has the least (median 1) due to the rearrangements involving the IGH locus. In all, 90.8% of IGH disease clones found by Sanger sequencing are also identified by RNA-Seq. In addition, RNA-Seq identified 43% more IGH disease clones. In 69 patients lacking sensitive IGH targets, targeted NGS IGH MRD showed high correlation (R = 0.93; P = 1.3 × 10−14), better relapse prediction than conventional RQ-PCR MRD using non-IGH targets. In conclusion, RNA-Seq can identify patient-specific clonal IGH junctional sequences for MRD monitoring, adding to its usefulness for molecular diagnosis in childhood B-ALL.

Joint inflammation is the common feature underlying juvenile idiopathic arthritis (JIA). Clinicians recognize patterns of joint involvement currently not part of the International League of Associations for Rheumatology (ILAR) classification. Using unsupervised machine learning, we sought to uncover data-driven joint patterns that predict clinical phenotype and disease trajectories. We analyzed prospectively collected clinical data, including joint involvement using a standard 71-joint homunculus, for 640 discovery patients with newly diagnosed JIA enrolled in a Canada-wide study who were followed serially for five years, treatment-naïve except for nonsteroidal anti-inflammatory drugs (NSAIDs) and diagnosed within one year of symptom onset. Twenty-one patients had systemic arthritis, 300 oligoarthritis, 125 rheumatoid factor (RF)-negative polyarthritis, 16 RF-positive polyarthritis, 37 psoriatic arthritis, 78 enthesitis-related arthritis (ERA), and 63 undifferentiated arthritis. At diagnosis, we observed global hierarchical groups of co-involved joints. To characterize these patterns, we developed sparse multilayer non-negative matrix factorization (NMF). Model selection by internal bi-cross-validation identified seven joint patterns at presentation, to which all 640 discovery patients were assigned: pelvic girdle (57 patients), fingers (25), wrists (114), toes (48), ankles (106), knees (283), and indistinct (7). Patterns were distinct from clinical subtypes (P < 0.001 by χ2 test) and reproducible through external data set validation on a 119-patient, prospectively collected independent validation cohort (reconstruction accuracy Q2 = 0.55 for patterns; 0.35 for groups). Some patients matched multiple patterns. To determine whether their disease outcomes differed, we further subdivided the 640 discovery patients into three subgroups by degree of localization-the percentage of their active joints aligning with their assigned pattern: localized (≥90%; 359 patients), partially localized (60%-90%; 124), or extended (<60%; 157). Localized patients more often maintained their baseline patterns (P < 0.05 for five groups by permutation test) than nonlocalized patients (P < 0.05 for three groups by permutation test) over a five-year follow-up period. We modelled time to zero joints in the discovery cohort using a multivariate Cox proportional hazards model considering joint pattern, degree of localization, and ILAR subtype. Despite receiving more intense treatment, 50% of nonlocalized patients had zero joints at one year compared to six months for localized patients. Overall, localized patients required less time to reach zero joints (partial: P = 0.0018 versus localized by log-rank test; extended: P = 0.0057). Potential limitations include the requirement for patients to be treatment naïve (except NSAIDs), which may skew the patient cohorts towards milder disease, and the validation cohort size precluded multivariate analyses of disease trajectories. Multilayer NMF identified patterns of joint involvement that predicted disease trajectory in children with arthritis. Our hierarchical unsupervised approach identified a new clinical feature, degree of localization, which predicted outcomes in both cohorts. Detailed assessment of every joint is already part of every musculoskeletal exam for children with arthritis. Our study supports both the continued collection of detailed joint involvement and the inclusion of patterns and degrees of localization to stratify patients and inform treatment decisions. This will advance pediatric rheumatology from counting joints to realizing the potential of using data available from uncovering patterns of joint involvement.

Leukemias are routinely sub-typed for risk/outcome prediction and therapy choice using acquired mutations and chromosomal rearrangements. Down syndrome acute lymphoblastic leukemia (DS‐ALL) is characterized by high frequency of CRLF2 ‐rearrangements, JAK2 ‐mutations, or RAS‐pathway mutations. Intriguingly, JAK2 and RAS -mutations are mutually exclusive in leukemic sub‐clones, causing dichotomy in therapeutic target choices. We prove in a cell model that elevated CRLF2 in combination with constitutionally active JAK2 is sufficient to activate wtRAS. On primary clinical DS‐ALL samples, we show that wtRAS-activation is an obligatory consequence of mutated/hyperphosphorylated JAK2. We further prove that CRLF2-ligand TSLP boosts the direct binding of active PTPN11 to wtRAS, providing the molecular mechanism for the wtRAS activation. Pre‐inhibition of RAS or PTPN11, but not of PI3K or JAK‐signaling, prevented TSLP‐induced RAS‐GTP boost. Cytotoxicity assays on primary clinical DS‐ALL samples demonstrated that, regardless of mutation status, high-risk leukemic cells could only be killed using RAS‐inhibitor or PTPN11-inhibitor, but not PI3K/JAK‐inhibitors, suggesting a unified treatment target for up to 80% of DS‐ALL. Importantly, protein activities-based principal-component-analysis multivariate clusters analyzed for independent outcome prediction using Cox proportional-hazards model showed that protein‐activity (but not mutation-status) was independently predictive of outcome, demanding a paradigm-shift in patient‐stratification strategy for precision therapy in high-risk ALL.