Explore the vast range of titles available.

Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient’s multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases. Machine learning models represent an opportunity for the automatic generation of histopathology reports. Here, the authors develop HistoGPT, a vision language model that can generate reports from multiple gigapixel-sized whole slide images and also predict tumour thickness, subtypes, and margins, among other diseases.

Monkeypox (mpox), a former rare viral zoonosis, has increasingly made it into the public eye since the major outbreak that started in May 2022. Mpox presents with skin lesions that change over time and go through different stages (macular, papular, pustular, and early and late ulceration). In this study, we evaluated skin biopsies of all stages. Therefore, five biopsies from four patients were analyzed histologically, immunohistochemically with anti-Vaccinia virus antibodies, and electron-microscopically. Notably, the early macular stage only showed subtle viropathic changes; it did not express of Orthopoxvirus proteins in immunohistochemistry and therefore can easily be missed histologically. In later stages, immunohistochemistry with anti-Vaccinia virus antibodies might be useful to distinguish mpox from differential diagnoses such as herpes virus infections. In the ulcerative stages, the identified occlusive vasculopathic changes could be an explanation for the severe pain of the lesions reported by some patients. Despite the small number of samples examined, our analysis suggests that the histological findings of mpox are highly dependent on the stage of the biopsied lesion. Therefore, knowledge of all different stages of histology is necessary to reliably diagnose mpox histologically, especially when molecular testing is not available.

Ultraviolet B (UVB) radiation induces mutagenic DNA photoproducts, in particular cyclobutane pyrimidine dimers (CPDs), in epidermal keratinocytes (KC). To prevent skin carcinogenesis, these DNA photoproducts must be removed by nucleotide excision repair (NER) or apoptosis. Here we report that the UVB-sensitive transcription factor aryl hydrocarbon receptor (AHR) attenuates the clearance of UVB-induced CPDs in human HaCaT KC and skin from SKH-1 hairless mice. Subsequent RNA interference and inhibitor studies in KC revealed that AHR specifically suppresses global genome but not transcription-coupled NER. In further experiments, we found that the accelerated repair of CPDs in AHR-compromised KC depended on a modulation of the p27 tumor suppressor protein. Accordingly, p27 protein levels were increased in AHR-silenced KC and skin biopsies from AHR −/− mice, and critical for the improvement of NER. Besides increasing NER activity, AHR inhibition was accompanied by an enhanced occurrence of DNA double-strand breaks triggering KC apoptosis at later time points after irradiation. The UVB-activated AHR thus acts as a negative regulator of both early defense systems against carcinogenesis, NER and apoptosis, implying that it exhibits tumorigenic functions in UVB-exposed skin. In fact, AHR −/− mice developed 50% less UVB-induced cutaneous squamous cell carcinomas in a chronic photocarcinogenesis study than their AHR +/+ littermates. Taken together, our data reveal that AHR influences DNA damage-dependent responses in UVB-irradiated KC and critically contributes to skin photocarcinogenesis in mice.

University resources are limited, and strategic admission management is required in certain fields that have high application volumes but limited available study places. Student admission processes need to select an appropriate number of applicants to ensure the optimal enrollment while avoiding over- or underenrollment. The traditional approach often relies on the enrollment yields from previous years, assuming fixed admission probabilities for all applicants and ignoring statistical uncertainty, which can lead to suboptimal decisions. In this study, we propose a novel machine-learning-based approach to improving student admission decisions. Trained on historical application data, this approach predicts the number of enrolled applicants conditionally based on the number of admitted applicants, incorporates the statistical uncertainty of these predictions, and derives the probability of the number of enrolled applicants being larger or smaller than the available study places. The application of this approach is illustrated using empirical application data from a German university. In this illustration, first, several machine learning models were trained and compared. The best model was selected. This was then applied to applicant data for the next year to estimate the individual enrollment probabilities, which were aggregated to predict the number of applicants enrolled and the probability of this number being larger or smaller than the available study places. When this approach was compared with the traditional approach using fixed enrollment yields, the results showed that the proposed approach enables data-driven adjustments to the number of admitted applicants, ensuring controlled risk of over- and underenrollment.

As in general pathology, digitalization is also inexorably making its way into dermatopathology. This article examines the current state of digitalization in German dermatopathology laboratories based on the authors' own experiences, the current study situation, and a survey of members of the Dermatological Histology Working Group (ADH). Experiences with the establishment of a digital laboratory workflow, artificial intelligence (AI)-based assistance systems, and whole slide images (WSI)-based training programs are discussed. Digitalization in dermatopathology is an opportunity to simplify and accelerate processes, but there are some hurdles to overcome.

A 43‐year‐old patient from Liberia presented with itching scaly plaques, impaired quality of life persisting for 9 months and intermittent joint pain in the knees. Personal and family history was unremarkable. Coexisting diagnoses included type 1 diabetes mellitus and liver cirrhosis due to chronic hepatitis B and D. The patient reported ongoing use of Tenofovir, Propranolol, Glargine and Aspart. Clinical examination showed disseminated brownish, partly confluent hyperkeratotic plaques distributed throughout the integument in a Fitzpatrick skin type V. A skin biopsy revealed histologically a psoriasiform dermatitis. Laboratory findings included lymphocytopenia and elevated liver values. Hepatitis B serology confirmed chronic hepatitis B. Considering the clinical and histopathological findings Psoriasis vulgaris was diagnosed and psoriatic arthritis was ruled out by a rheumatologist. After consulting with the hepatologist, systemic therapy with Apremilast was initiated. Despite an initial positive response, gastrointestinal side effects led to a switch to therapy with Tildrakizumab, with regular monitoring of transaminases and HBV DNA. Despite an initial positive treatment response, a secondary loss of efficacy occurred prompting a switch to Risankizumab. Psoriasis is a chronic inflammatory systemic disease significantly impacting quality of life, necessitating optimal long‐term management. In this case, therapy with Apremilast and biologic therapy targeting IL‐23 blockade, even with concurrent hepatitis B, was well tolerated and effective. However, the evidence on the safety of modern psoriasis therapies in known chronic infections remains limited. Noteworthy in this case report is the patient's dark skin type. Diagnosing inflammatory dermatoses in People of Colour is challenging due to less visible erythema and other clinical nuances, exacerbating the already poorer healthcare for People of Colour. This case aims to raise awareness about the need for increased representation of People of Colour in education, presentations and clinical trials to ensure better care for this patient population. A 43‐year‐old Liberian patient presented with itching scaly plaques and known hepatitis B and D. Histologically and clinically a Psoriasis vulgaris was diagnosed. The initial therapy with Apremilast was switched to Tildrakizumab due to side effects, and later to Risankizumab due to secondary loss of efficacy. Managing psoriasis in dark skin is challenging due to subtle clinical signs. This case highlights the need for better representation of People of Color in medical education and research for improved care.

Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.

ABSTRACTTherapeutic options of anogenital warts (AGW) at the urethral meatus are limited and often require effortful and time-consuming procedures under general anesthesia. Here, we present two cases of AGW at the urethral meatus, which we have successfully treated with low-dose topical ingenol mebutate-gel.

In a pooled analysis of nine clinical trials involving almost 5000 women with breast cancer who underwent examination of the bone marrow for metastatic cancer cells, the presence of metastases in the bone marrow at the time of diagnosis of breast cancer was associated with a poor prognosis. In trials involving almost 5000 women with breast cancer, the presence of micrometastases in the bone marrow at the time of diagnosis of breast cancer was associated with a poor prognosis. Data from experiments in animals 1 performed in the 1960s and from more recent immunocytochemical 2 , 3 and molecular 4 , 5 studies suggest that lymph-node involvement does not accurately predict hematogenous dissemination of cancer cells, nor is hematogenous dissemination necessarily associated with lymph-node involvement. 6 , 7 During the past two decades, several studies have assessed the prevalence and prognostic value of hematogenous dissemination of tumor cells in patients with node-positive and node-negative breast cancer. 3 , 8 – 15 The influence of the presence of micrometastasis in the bone marrow on prognosis has been shown in patients with identical stages of breast cancer, as defined by tumor . . .