Indocyanine Green in Bariatric Surgery: a Systematic Review.

Indocyanine green (ICG) is a fluorescent dye that can be used intraoperatively to assess tissue perfusion, as well as perform leak testing. This study aims to summarize published manuscripts on outcomes of ICG use and reduction of complications compared to traditional leak test and tissue perfusion evaluation. A PubMed search using "ICG and bariatric surgery," "ICG and gastric sleeve," "ICG and gastric bypass," and "ICG and revisional bariatric surgery" was performed. The proportion of patients who underwent an intraoperative decision change due to ICG was 3.8% (95% CI: 2.0 to 7.2%). ICG fluorescent imaging in bariatric surgery is a valuable tool, and further studies are needed to confirm its utility for routine use in both standard or complex cases (PROSPERO #418126).

Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication.

The safeguarding of online services and prevention of unauthorized access by hackers rely heavily on user authentication, which is considered a crucial aspect of security. Currently, multi-factor authentication is used by enterprises to enhance security by integrating multiple verification methods rather than relying on a single method of authentication, which is considered less secure. Keystroke dynamics is a behavioral characteristic used to evaluate an individual's typing patterns to verify their legitimacy. This technique is preferred because the acquisition of such data is a simple process that does not require any additional user effort or equipment during the authentication process. This study proposes an optimized convolutional neural network that is designed to extract improved features by utilizing data synthesization and quantile transformation to maximize results. Additionally, an ensemble learning technique is used as the main algorithm for the training and testing phases. A publicly available benchmark dataset from Carnegie Mellon University (CMU) was utilized to evaluate the proposed method, achieving an average accuracy of 99.95%, an average equal error rate (EER) of 0.65%, and an average area under the curve (AUC) of 99.99%, surpassing recent advancements made on the CMU dataset.

Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications.

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call "Hand Activity Recognition through using a Convolutional neural network with Spectrograms" (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R2 = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.

Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review.

OBJECTIVES: To estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective during the early phases of the pandemic. DESIGN: Systematic review. METHODS: Two parallel searches of academic bibliographic databases and grey literature were undertaken until 8 May 2020. Governments were also contacted for further information where possible. There were no restrictions on language, information sources used, publication status and types of sources of evidence. The AACODS checklist or the National Institutes of Health study quality assessment tools were used to appraise each source of evidence. OUTCOME MEASURES: Publication characteristics, country-specific data points, COVID-19-specific data, demographics of affected HCWs and public health measures employed. RESULTS: A total of 152 888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%, n=14 058) and nurses (38.6%, n=10 706), but deaths were mainly in men (70.8%, n=550) and doctors (51.4%, n=525). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.2 deaths reported per 100 infections for HCWs aged over 70 years. Europe had the highest absolute numbers of reported infections (119 628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7). CONCLUSIONS: COVID-19 infections and deaths among HCWs follow that of the general population around the world. The reasons for gender and specialty differences require further exploration, as do the low rates reported in Africa and India. Although physicians working in certain specialities may be considered high risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs.

Urological Manifestations of Placenta Percreta.

Placenta percreta is a condition of pregnancy associated with abnormal decidua placenta. It is characterized by invasion of chorionic villi past the myometrium and serosa, towards urogenital organs. Complications include massive hemorrhage, bladder dysfunction, and severe infections during delivery. Reports suggest an increasing prevalence of this condition. From a urological perspective, this review suggests how early diagnostic modalities, effective treatment plans, and appropriate surgical methods may aid in decreasing the morbidity and mortality of placenta percreta. The importance of maintaining bladder integrity during hysterectomy is emphasized.

Coupling a high-temperature catalytic oxidation total organic carbon analyzer to an isotope ratio mass spectrometer to measure natural-abundance delta13C-dissolved organic carbon in marine and freshwater samples.

The stable isotope composition of dissolved organic carbon (delta(13)C-DOC) provides powerful information toward understanding carbon sources and cycling, but analytical limitations have precluded its routine measurement in natural samples. Recent interfacing of wet oxidation-based dissolved organic carbon analyzers and isotope ratio mass spectrometers has simplified the measurement of delta(13)C-DOC in freshwaters, but the analysis of salty estuarine/marine samples still proves difficult. Here we describe the coupling of the more widespread high-temperature catalytic oxidation-based total organic carbon analyzer to an isotope ratio mass spectrometer (HTC-IRMS) through cryogenic trapping of analyte gases exiting the HTC analyzer for routine analysis of delta(13)C-DOC in aquatic and marine samples. Targeted elimination of major sources of background CO2 originating from the HTC analyzer allows for the routine measurement of samples over the natural range of DOC concentrations (from 40 microM to over 2000 microM), and salinities (<0.1-36 g/kg). Because consensus reference natural samples for delta(13)C-DOC do not exist, method validation was carried out with water-soluble stable isotope standards as well as previously measured natural samples (IAEA sucrose, Suwannee River Fulvic Acids, Deep Sargasso Sea consensus reference material, and St. Lawrence River water) and result in excellent delta(13)C-DOC accuracy (+/-0.2 per thousand) and precision (+/-0.3 per thousand).