Anorectal balloon cell melanoma: a rare variant

Balloon cell melanoma is a rare presentation of malignant melanoma, usually on the skin, with less than 100 cases reported. Mucosal BCM is even rarer, with only one case of anorectal BCM reported in English literature. The diagnosis is based on the histopathologic findings of a tumor composed of large, foamy melanocytes, with or without pigmentation, and confirmed by immunohistochemical studies showing expression for melanocytic markers. The foam cell appearance of the tumor cells and the lack of melanin pigment lead to a diagnostic dilemma, mostly when presented at an unusual location. Herein, we report a case of balloon cell melanoma at the anorectal junction in a 73-year-old male patient complaining of constipation and bleeding per rectum. Surgical resection was performed with no evidence of recurrence after three years of close follow-up. We believe this case will raise awareness among the medical community to consider this tumor a differential diagnosis in rectal masses

Comparative performance study in multiplexed RZDPSK for SMF's with FBG

The Fiber Bragg gratings have emerged as important components in several of light wave applications in the FBG becoming a ubiquitous and necessary element in equipment located throughout the network from the central office to the home. This paper explores comparative performance study with and without using FBG as an external dispersion compensator for sixteen channel return to zero differential phase shift keying modulation operating at 45Gbps per channel with channel spacing of 0.15nm. Simulations are done with various single mode fibers with and without external FBG.Better performance (Q, BER) for dispersion values used in simulation are -58ps/nm, 23ps/nm, and 100ps/nm for FBG's used at receiver channels. It is observed that FBG used with receiver channels support larger communication fiber length, also G655 (NZDSF) fiber shows much better performance as compared with other SMF's tested. Key Words: FBG, WDM, DCG, DCF, FOM, RZ

A study to correlate histopathological findings in patients with abnormal uterine bleeding

Background: Abnormal uterine bleeding (AUB) is a common gynecological problem associated with considerable morbidity and significantly affects the patients. The aim of the study was to analyze the histopathological patterns of endometrium in patients presenting with AUB and also to determine the incidence of AUB in various age groups.Methods: This is a retrospective study, conducted in the Department of Obstetrics and Gynaecology, in a tertiary care teaching hospital, Mumbai, India from March 2016 till date. All cases of AUB were included in the study. Data was entered in microsoft excel and managed in statistical package for the social sciences (SPSS) version 16. Analysis was done in the form of percentages and proportions and represented as tables where necessary.Results: A total of 120 cases were analyzed. Patients’ age ranged from 22-79 years. AUB was most common among the perimenopausal females (41-50years). The most common presenting symptom was heavy menstrual bleeding (53%). Dilatation and curettage (D&C) was performed in all cases and 96 underwent hysterectomy as final resort. Endometrial proliferative pattern was the most common histopathological finding and was seen in 27% patients, followed by endometrial hyperplasia in 13.5% patients, secretory endometrium (12.7%) and disordered proliferative endometrium were seen in 10.9% patients each. Malignancy was detected in 1.7% of cases and endometrial carcinoma was the most common lesion.Conclusions: Endometrial sampling is especially indicated in women above the age of 35 years to rule out malignancy and preneoplasia. Among the females with no organic pathology, normal physiological patterns with proliferative, secretory, and menstrual changes were observed. The most common endometrial pathology in this study was endometrial proliferation

Investigation of all-optical inverter system with NRZ and RZ modulation formats at 100 Gbit/s

U radu se predstavlja komparativno istraživanje formata modulacije ne-vraćanje-na-nulu (non-return-to-zero) i vraćanje-na-nulu (return-to-zero) za ulazu optičkog pretvarača kod 100 Gbit/s. Uspoređuju se različite performanse kao što su dijagram oka, omjer gašenja i učestalost pogrešnih bitova kod različite učestalosti bitova na osnovu prikladnosti formata podataka. To pokazuje da su impulsi vraćanja na nulu promijenjeni po sličnom uzorku te je stoga učestalost pogrešnih bitova bolja od formata non-return-to-zero. Na format non-return-to-zero više djeluje nelinearnost optičkog pojačala poluvodiča te je stoga degradirana učestalost pogrešnih bitova, a poboljšan omjer gašenja (ekstinkcije).The paper presents the comparative investigation on non-return-to-zero and return-to-zero modulation formats for optical inverter gate at 100 Gbit/s. Various performances as eye pattern, extinction ratio and bit error rate at different bit-rate are compared based on suitability of data formats. It indicates that return-to-zero pulses are distorted in a similar pattern, therefore bit error rate is better than non-return-to-zero format. The non-return-to-zero is more affected by non-linearity of semiconductor optical amplifier hence the bit error rate is degraded and extinction ratio is improved

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing