First national survey of anti-tuberculosis drug resistance in Azerbaijan and risk factors analysis.

SETTING: Civilian population of the Republic of Azerbaijan. OBJECTIVES: To determine patterns of anti-tuberculosis drug resistance among new and previously treated pulmonary tuberculosis (TB) cases, and explore their association with socio-demographic and clinical characteristics. DESIGN: National cross-sectional survey conducted in 2012-2013. RESULTS: Of 789 patients (549 new and 240 previously treated) who met the enrolment criteria, 231 (42%) new and 146 (61%) previously treated patients were resistant to any anti-tuberculosis drug; 72 (13%) new and 66 (28%) previously treated patients had multidrug-resistant TB (MDR-TB). Among MDR-TB cases, 38% of new and 46% of previously treated cases had pre-extensively drug-resistant TB (pre-XDR-TB) or XDR-TB. In previously treated cases, 51% of those who had failed treatment had MDR-TB, which was 15 times higher than in relapse cases (OR 15.2, 95%CI 6-39). The only characteristic significantly associated with MDR-TB was a history of previous treatment (OR 3.1, 95%CI 2.1-4.7); for this group, history of incarceration was an additional risk factor for MDR-TB (OR 2.8, 95%CI 1.1-7.4). CONCLUSION: Azerbaijan remains a high MDR-TB burden country. There is a need to implement countrywide control and innovative measures to accelerate early diagnosis of drug resistance in individual patients, improve treatment adherence and strengthen routine surveillance of drug resistance

Therapeutic Potential of Ralimetinib, a p38/MAPK14 Inhibitor, in Anaplastic Thyroid Cancer

View full abstracthttps://openworks.mdanderson.org/leading-edge/1028/thumbnail.jp

Calcium-sensing receptor autoantibody-mediated hypoparathyroidism associated with immune checkpoint inhibitor therapy : diagnosis and long-term follow-up

Background Immune checkpoint inhibitors (ICIs) have produced significant survival benefit across many tumor types. However, immune-related adverse events are common including autoimmune responses against different endocrine organs. Here, a case of ICI-mediated hypoparathyroidism focusing on long-term follow-up and insights into its etiology is presented. Case and methods A 73-year-old man developed severe symptomatic hypocalcemia after the initiation of ipilimumab and nivolumab for the treatment of metastatic melanoma. Hypoparathyroidism was diagnosed with undetectable intact parathyroid hormone (PTH). Immunoprecipitation assays, ELISAs, and cell-based functional assays were used to test the patient for antibodies against the calcium-sensing receptor (CaSR). NACHT leucine-rich repeat protein 5 (NALP5) and cytokine antibodies were measured in radioligand binding assays and ELISAs, respectively. Results The patient’s symptoms improved with aggressive calcium and vitamin D supplementation. At 3 years and 3 months since the diagnosis of hypoparathyroidism, PTH was still inappropriately low at 7.6 pg/mL, and attempted discontinuation of calcium and calcitriol resulted in recurrent symptomatic hypocalcemia. Analysis for an autoimmune etiology of the patient’s hypoparathyroidism indicated that CaSR antibodies were negative before treatment and detected at multiple time points afterwards, and corresponded to the patient’s clinical course of hypoparathyroidism. CaSR antibodies purified from the patient’s serum activated the human CaSR. The patient was seronegative for NALP5 and cytokine antibodies, indicating that their hypoparathyroidism was not a manifestation of autoimmune polyendocrine syndrome type 1. Conclusion The etiology of hypocalcemia is likely autoimmune hypoparathyroidism caused by the development of CaSR-activating antibodies that might prevent PTH release from the parathyroid

Characterization of Altered immunity in Anaplastic Thyroid Cancer

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Managing toxicities associated with immune checkpoint inhibitors: consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group.

Cancer immunotherapy has transformed the treatment of cancer. However, increasing use of immune-based therapies, including the widely used class of agents known as immune checkpoint inhibitors, has exposed a discrete group of immune-related adverse events (irAEs). Many of these are driven by the same immunologic mechanisms responsible for the drugs\u27 therapeutic effects, namely blockade of inhibitory mechanisms that suppress the immune system and protect body tissues from an unconstrained acute or chronic immune response. Skin, gut, endocrine, lung and musculoskeletal irAEs are relatively common, whereas cardiovascular, hematologic, renal, neurologic and ophthalmologic irAEs occur much less frequently. The majority of irAEs are mild to moderate in severity; however, serious and occasionally life-threatening irAEs are reported in the literature, and treatment-related deaths occur in up to 2% of patients, varying by ICI. Immunotherapy-related irAEs typically have a delayed onset and prolonged duration compared to adverse events from chemotherapy, and effective management depends on early recognition and prompt intervention with immune suppression and/or immunomodulatory strategies. There is an urgent need for multidisciplinary guidance reflecting broad-based perspectives on how to recognize, report and manage organ-specific toxicities until evidence-based data are available to inform clinical decision-making. The Society for Immunotherapy of Cancer (SITC) established a multidisciplinary Toxicity Management Working Group, which met for a full-day workshop to develop recommendations to standardize management of irAEs. Here we present their consensus recommendations on managing toxicities associated with immune checkpoint inhibitor therapy

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

Mechanisms and treatment of ischaemic stroke: insights from genetic associations

The precise pathophysiology of ischaemic stroke is unclear, and a greater understanding of the different mechanisms that underlie large-artery, cardioembolic and lacunar ischaemic stroke subtypes would enable the development of more-effective, subtype-specific therapies. Genome-wide association studies (GWASs) are identifying novel genetic variants that associate with the risk of stroke. These associations provide insight into the pathophysiological mechanisms, and present opportunities for novel therapeutic approaches. In this Review, we summarize the genetic variants that have been linked to ischaemic stroke in GWASs to date and discuss the implications of these associations for both our understanding and treatment of ischaemic stroke. The majority of genetic variants identified are associated with specific subtypes of ischaemic stroke, implying that these subtypes have distinct genetic architectures and pathophysiological mechanisms. The findings from the GWASs highlight the need to consider whether therapies should be subtype-specific. Further GWASs that include large cohorts are likely to provide further insights, and emerging technologies will complement and build on the GWAS findings