Single Stage Transoral Laser Microsurgery for Early Glottic Cancer

Objectives: The purpose of the study was to present the outcome of our management protocol of a single stage transoral laser microsurgery (SSTLM), with the intention of complete removal of a lesion, considered to be an early glottic cancer.Methods: Between January 2015 to February 2017 patients with the clinical appearance of an early glottic cancer, who were candidates for (SSTLM) management protocol, were included in this study. Type of cordectomy was determined by pre- and intra-operative evaluation of the extent of lesion in cord layers.Results: Thirty patients (6 females, 24 males; mean age 65 years) underwent SSTLM. Twenty-two patients had malignant histopathological diagnosis of severe dysplasia or Cis in 4 patients, microinvasice carcinoma in 3 patients and invasive carcinoma in 15 patients (T1a tumor in 14 and T1b tumor in 1). Eight patients had a nonmalignant histological diagnosis of keratosis without atypia in 2 patients, mild dysplasia in 2 patients and moderate dysplasia in 3 patients. Based on pre- and intra-operative evaluation, 14 subepithelial (type I), 10 subligamental (type II), and 6 transmuscular (type III) cordectomies were performed. Comparison of cordectomies types with postoperative histopathologic diagnosis showed an adequate extent of resection in 26 out of 30 patients (87%). Considering only patients without recent background of direct laryngoscopy and biopsy, an adequate resection was performed in 90% of patients. None of the patients was further treated by external beam radiation. At average follow-up of 21 months, none of the patients developed local recurrence.Conclusion: In selected cases, a SSTLM for clinical appearance of an early glottic cancer, allows a reliable histopathologic diagnosis and a high local control rate with favorable cost effectiveness. A careful pre- and intraoperative evaluation for selecting the appropriate cases for this management is required in order to avoid under- or over-treatment

Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy

Case report: Blindness associated with Learedius learedi trematode infection in a green sea turtle, Chelonia mydas, of the northern Red Sea

Spirorchiid blood flukes are widespread in sea turtles, causing disease and mortality in their populations, with high prevalence in several ocean basins. Besides being leading parasitic causes of sea turtle strandings in several parts of the world, these infectious agents can cause endocarditis, vasculitis, thrombosis, miliary egg granulomas, and aneurysms, which ultimately may compromise the survival of green sea turtles. More severe cases may also result in multifocal granulomatous meningitis or pneumonia, both of which can be fatal. Herein, we report the first case of severe trematode infection, Caused by Learedius learedi, in a green sea turtle in the northern Red Sea; this infection is associated with bilateral blindness. Necropsy revealed multiple granulomas with intralesional trematode eggs in the optic nerve, eyes, spleen, heart, and lungs. The parasite was identified as Learedius learedi through specific primers of the ribosomal genome and COI sequences obtained from GenBank. Altogether, these findings emphasize the importance of recognizing the systemic nature of this particular fluke infection to ultimately protect the lives of these marine animals and ensure the sustainability of these species in the wild

Entangled Stories: The Red Jews in Premodern Yiddish and German Apocalyptic Lore

“Far, far away from our areas, somewhere beyond the Mountains of Darkness, on the other side of the Sambatyon River…there lives a nation known as the Red Jews.” The Red Jews are best known from classic Yiddish writing, most notably from Mendele's Kitser masoes Binyomin hashlishi (The Brief Travels of Benjamin the Third). This novel, first published in 1878, represents the initial appearance of the Red Jews in modern Yiddish literature. This comical travelogue describes the adventures of Benjamin, who sets off in search of the legendary Red Jews. But who are these Red Jews or, in Yiddish, di royte yidelekh? The term denotes the Ten Lost Tribes of Israel, the ten tribes that in biblical times had composed the Northern Kingdom of Israel until they were exiled by the Assyrians in the eighth century BCE. Over time, the myth of their return emerged, and they were said to live in an uncharted location beyond the mysterious Sambatyon River, where they would remain until the Messiah's arrival at the end of time, when they would rejoin the rest of the Jewish people. This article is part of a broader study of the Red Jews in Jewish popular culture from the Middle Ages through modernity. It is partially based on a chapter from my book, Umstrittene Erlöser: Politik, Ideologie und jüdisch-christlicher Messianismus in Deutschland, 1500–1600 (Göttingen: Vandenhoeck & Ruprecht, 2011). Several postdoctoral fellowships have generously supported my research on the Red Jews: a Dr. Meyer-Struckmann-Fellowship of the German Academic Foundation, a Harry Starr Fellowship in Judaica/Alan M. Stroock Fellowship for Advanced Research in Judaica at Harvard University, a research fellowship from the Heinrich Hertz-Foundation, and a YIVO Dina Abramowicz Emerging Scholar Fellowship. I thank the organizers of and participants in the colloquia and conferences where I have presented this material in various forms as well as the editors and anonymous reviewers of AJS Review for their valuable comments and suggestions. I am especially grateful to Jeremy Dauber and Elisheva Carlebach of the Institute for Israel and Jewish Studies at Columbia University, where I was a Visiting Scholar in the fall of 2009, for their generous encouragement to write this article. Sue Oren considerably improved my English. The style employed for Romanization of Yiddish follows YIVO's transliteration standards. Unless otherwise noted, translations from the Yiddish, Hebrew, German, and Latin are my own. Quotations from the Bible follow the JPS translation, and those from the Babylonian Talmud are according to the Hebrew-English edition of the Soncino Talmud by Isidore Epstein

Recovery Rates of Treated vs. Non-Treated Dairy Cows with Subclinical Mastitis

The term “spontaneous recovery” refers to a return to a previous condition without any external treatment. In cow mastitis, it refers to cases exhibiting visual symptoms (clinical) or an increase in somatic cell count (SCC) with no visual symptoms (subclinical), with or without identification of a pathogen, from which the animal recovers. A large retrospective analysis of data compiled from the Israeli Dairy Herd Book was performed to evaluate the occurrence of: (i) actual “spontaneous recovery” from the inflammation; (ii) recovery from the inflammation due to antibiotic treatment. In 2018, 123,958 cows from 650 herds with first elevation of SCC at monthly test-day milk yield were clustered into five SCC-cutoff levels (CL) (×103 cells/mL): CL1 (200–299), CL2 (300–399), CL3 (400–499), CL4 (500–999), CL5 (≥1000). Each cutoff level was analyzed separately, and each cow appeared only once in the same lactation and cutoff level, thus resulting in five independent analyses. Recovery was defined as decreased SCC on all three monthly test days, or on the second and third test days, set to: R1 (3 cells/mL); R2 (3 cells/mL). No difference was found among cutoff levels when the recovery was set to R1, with only 10–12% of the cows presenting spontaneous recovery. When the recovery was set to R2, percent spontaneous recovery was 25–27% at the three higher cutoff levels (CL3–CL5) and 35–41% at the lowest levels (CL1, CL2). Antibiotic treatment was administered to only ~10% of the cows, and in only the higher cutoff-level groups—CL4 and CL5. No difference was found between spontaneous recovery and recovery after antibiotic treatment. Moreover, percentage culled cows treated with antibiotics was significantly higher (p < 0.01) than that of non-treated culled cows (18 and 10.2, respectively), suggesting that the more severe mastitis cases were treated. We concluded that (i) actual spontaneous recovery from inflammation is low and does not depend on the number of cells in the milk at time of infection, and (ii) recovery from inflammation following antibiotic treatment is not higher

Estimating variance components in population scale family trees.

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records