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Cantharidin, a natural toxin produced by the blister beetle, is a topical agent that induces acantholysis of the epidermis, breaking down desmosome plaques through the release of serine proteases. Cantharidin is available in three liquid forms: Ycanth (0.7%), Canthacur (0.7%), and Canthacur PS (1% cantharidin, 30% salicylic acid, 2% podophyllotoxin). Ycanth is used to treat molluscum contagiosum (MC). Canthacur is routinely used to treat a variety of dermatologic conditions including MC, plantar warts, and common warts, whereas Canthacur PS is a more potent formulation indicated for treatment of plantar warts only. The objective of this review is to highlight the efficacy, safety, and diverse use of topical cantharidin in the treatment of various skin conditions. Conditions in which treatment with topical cantharidin yielded a good-to-excellent response include MC, plantar warts, and common warts. Topical cantharidin treatment of anogenital warts yielded mixed results. None of the indications reviewed herein yielded a poor response to topical cantharidin. Overall, topical cantharidin resulted in a good-to-excellent clinical response in several conditions with mild and transient adverse events. The results of this review suggest the safe and efficacious use of topical cantharidin in the field of dermatology and highlight the potential for future use.

BackgroundThere is little information on the incidence of AIDS-defining events which have been reported in the literature to be associated with immune reconstitution inflammatory syndrome (IRIS) after combined antiretroviral therapy (cART) initiation. These events include tuberculosis, mycobacterium avium complex (MAC), cytomegalovirus (CMV) retinitis, progressive multifocal leukoencephalopathy (PML), herpes simplex virus (HSV), Kaposi sarcoma, non-Hodgkin lymphoma (NHL), cryptococcosis and candidiasis.MethodsWe identified individuals in the HIV-CAUSAL Collaboration, which includes data from six European countries and the US, who were HIV-positive between 1996 and 2013, antiretroviral therapy naive, aged at least 18 years, had CD4 cell count and HIV-RNA measurements and had been AIDS-free for at least 1 month between those measurements and the start of follow-up. For each AIDS-defining event, we estimated the hazard ratio for no cART versus less than 3 and at least 3 months since cART initiation, adjusting for time-varying CD4 cell count and HIV-RNA via inverse probability weighting.ResultsOut of 96 562 eligible individuals (78% men) with median (interquantile range) follow-up of 31 [13,65] months, 55 144 initiated cART. The number of cases varied between 898 for tuberculosis and 113 for PML. Compared with non-cART initiation, the hazard ratio (95% confidence intervals) up to 3 months after cART initiation were 1.21 (0.90-1.63) for tuberculosis, 2.61 (1.05-6.49) for MAC, 1.17 (0.34-4.08) for CMV retinitis, 1.18 (0.62-2.26) for PML, 1.21 (0.83-1.75) for HSV, 1.18 (0.87-1.58) for Kaposi sarcoma, 1.56 (0.82-2.95) for NHL, 1.11 (0.56-2.18) for cryptococcosis and 0.77 (0.40-1.49) for candidiasis.ConclusionWith the potential exception of mycobacterial infections, unmasking IRIS does not appear to be a common complication of cART initiation in high-income countries.

USDA National Agricultural Statistics Service general crop condition data are used to quantify the relationship between crop conditions and yield, condition tendencies during the growing season, how conditions vary across the United States, and how conditions have trended for the 1986–2022 period for a variety of crops: barley (Hordeum vulgare L.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), oats (Avena sativa L.), peanuts (Arachis hypogaea L.), rice (Oryza sativa L.), sorghum (Sorghum bicolor L.), soybeans (Glycine max L.), and spring and winter wheat (Triticum aestivum L.). Crop conditions are a statistically significant covariate (90% significance level) for yield and can be used in practice on a weekly basis. This becomes increasingly important as, historically, crop conditions not only deteriorated on average, but also became more variable throughout the growing season. Spatial differences in conditions, variability, and trends in condition ratings and variability are caused by crop selection, regional climate, and management practices such as irrigation. Crop conditions were poorest and had the highest interannual variability across the southern Great Plains region. Since 1986, crop conditions in the Great Plains region have deteriorated significantly, while variability has increased. Meanwhile, crop conditions across the southwestern and southeastern United States have significantly improved, while interannual variability in conditions has experienced robust declines. Given climatic and hydrologic trends, to maintain a positive trend in yield, continued advancements in production management and technology are required. Critical decisions regarding real‐time or future management can be supplemented by using crop condition risk assessment information along with actively monitoring weekly conditions. Core Ideas: USDA crop condition ratings are a statistically significant covariate for yield and can be used in practice.Crop conditions tend to deteriorate as the season progresses while historically variability increases.Crop conditions were poorest on average and had the highest interannual variability in the southern Great Plains.Since 1986, crop conditions in the Great Plains have deteriorated significantly while variability has increased.The southwestern and southeastern United States significantly improved in crop conditions, while variability decreased. [ABSTRACT FROM AUTHOR]

While bariatric surgery is an effective method for achieving long-term weight loss, postoperative readmissions are associated with negative clinical outcomes and significant costs. We aimed to use machine learning (ML) algorithms to predict readmissions and compare results to logistic regression. Hospitals participating in the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program, United States. Patients who underwent sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and biliopancreatic diversion with duodenal switch between 2016 and 2020 were selected from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database. Patient variables reported by the MBSAQIP database were analyzed by ML algorithms random forest (RF), gradient boosting (XGB), and deep neural networks (NN), and the results of the predictive models were compared to logistic regression using area under the receiver operating characteristic curve (AUROC). Our study included 863,348 patients, of which 39,068 (4.52%) were readmitted. AUROC scores were XGB.785 (95% CI.784–.786), RF.785 (95% CI.784–.785), and NN.754 (95% CI.753–.754), compared with.62 (95% CI.62–.621) for logistic regression (LR) (P <.001). The sensitivity and specificity for XGB, the best performing model, were 73.81% and 70%, compared with 52.94% and 70% for logistic regression. The most important variables were intervention or reoperation prior to discharge, unplanned ICU admission, initial procedure, and the intraoperative transfusion. ML demonstrates significant advantages over logistic regression when predicting 30-day readmission following bariatric surgery. With external validation, models could identify the best candidates for early discharge or targeted postdischarge resources. • Machine learning (ML) predicted readmissions after bariatric surgery better than logistic regression when using all predischarge information. • All model types showed low predictive ability for readmissions based on pre and intraoperative factors alone. [ABSTRACT FROM AUTHOR]