Notes

Burr-hole Cleansing with Closed-system Waterflow and drainage to treat Long-term Subdural Hematoma: The Meta-analysis.
The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi-omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real-world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.Artificial intelligence (AI) is a branch of computer science that attempts to mimic human intelligence, such as learning and problem-solving skills. The use of AI in hepatology occurred later than in gastroenterology. check details Nevertheless, studies on applying AI to liver disease have recently increased. AI in hepatology can be applied for detecting liver fibrosis, differentiating focal liver lesions, predicting prognosis of chronic liver disease, and diagnosing of nonalcoholic fatty liver disease. We expect that AI will eventually help manage patients with liver disease, predict the clinical outcomes, and reduce medical errors. However, there are several hurdles that need to be overcome. Here, we will briefly review the areas of liver disease to which AI can be applied.Die Tumeszenz-Lokalanästhesie (TLA) spielt bei dermatochirurgischen Eingriffen eine wichtige Rolle. Die TLA bietet etliche Vorteile, wie lang anhaltende Betäubung, reduzierte Blutung während der Operation und Vermeidung möglicher Komplikationen einer Vollnarkose. Einfache Durchführung, günstiges Risikoprofil und breites Indikationsspektrum sind weitere Gründe dafür, dass TLA zunehmend auch bei Säuglingen eingesetzt wird. Es gibt nicht nur viele Indikationen für chirurgische Exzisionen im Säuglingsalter, wie angeborene Naevi, sondern es hat auch erhebliche Vorteile, wenn diese Exzisionen in einem frühen Alter durchgeführt werden. Dazu zählen die geringere Größe der Läsionen sowie die unproblematische Wundheilung und Geweberegeneration im Säuglingsalter. Dennoch müssen hinsichtlich der Anwendung der TLA bei Säuglingen einige Aspekte berücksichtigt werden, darunter die Dosierung, eine veränderte Plasmaproteinbindung und die Notwendigkeit einer adäquaten und lang anhaltenden Schmerzkontrolle.
Primär kutane Lymphome (PCL) unterscheiden sich oft stark im klinischen Verhalten und in der Prognose von systemischen Lymphomen des gleichen histopathologischen Typs. Ziel der Studie war es, die Verteilung der PCL-Subtypen, die Zeitspanne von der Krankheitsmanifestation bis zur Diagnosestellung, den Stellenwert diagnostischer Verfahren, das Auftreten von Zweittumoren und die verschiedenen Behandlungsmodalitäten im Rahmen des Krankheitsverlaufs zu untersuchen.

Retrospektive Analyse von 152 Patienten mit PCL, die von 2010-2012 an der Universitäts-Hautklinik Tübingen behandelt wurden.

105 Patienten mit primär kutanem T-Zell-Lymphom (CTCL) (69,1%) und 47 Patienten mit primär kutanem B-Zell-Lymphom (CBCL) (30,9%) wurden eingeschlossen. Die Zeitspanne von der Krankheitsmanifestation bis zur Diagnose betrug durchschnittlich vier Jahre. Mycosis fungoides (MF) (47,4%) wurde am häufigsten diagnostiziert. Die First-Line-Therapien umfassten hier entweder eine alleinige Phototherapie (PUVA, n=48; UVB 311nm, n=7) oder Kombinationstherapien (PUVA mit systemischen Retinoiden, n=18). Häufigste Second-Line-Therapie war Interferon (INF)-α plus PUVA (n=15). Der Behandlungsverlauf war insgesamt günstig (45,2% Remission, 28,6% stabile Erkrankung, 22,6% Progress). Maligne Komorbiditäten wurden im Vergleich zu einer gesunden Vergleichsgruppe häufiger beobachtet.

Bis zur Diagnosestellung der PCL dauert es oft mehrere Jahre. Der Wert der Staging-Verfahren ist gering. Die Behandlungsmodalitäten in früheren MF-Stadien basieren hauptsächlich auf der Phototherapie.
Bis zur Diagnosestellung der PCL dauert es oft mehrere Jahre. Der Wert der Staging-Verfahren ist gering. Die Behandlungsmodalitäten in früheren MF-Stadien basieren hauptsächlich auf der Phototherapie.Morphology-control synthesis is an effective means to tailor surface structure of noble-metal nanocrystals, which offers a sensitive knob for tuning their electrocatalytic properties. The functional molecules are often indispensable in the morphology-control synthesis through preferential adsorption on specific crystal facets, or controlling certain crystal growth directions. In this review, the recent progress in morphology-control synthesis of noble-metal nanocrystals assisted by amino-based functional molecules for electrocatalytic applications are focused on. Although a mass of noble-metal nanocrystals with different morphologies have been reported, few review studies have been published related to amino-based molecules assisted control strategy. A full understanding for the key roles of amino-based molecules in the morphology-control synthesis is still necessary. As a result, the explicit roles and mechanisms of various types of amino-based molecules, including amino-based small molecules and amino-based polymers, in morphology-control of noble-metal nanocrystals are summarized and discussed in detail. Also presented in this progress are unique electrocatalytic properties of various shaped noble-metal nanocrystals. Particularly, the optimization of electrocatalytic selectivity induced by specific amino-based functional molecules (e.g., polyallylamine and polyethyleneimine) is highlighted. At the end, some critical prospects, and challenges in terms of amino-based molecules-controlled synthesis and electrocatalytic applications are proposed.From an "over-engineering" era in which biomaterials played a central role, now it is observed to the emergence of "developmental" tissue engineering (TE) strategies which rely on an integrative cell-material perspective that paves the way for cell self-organization. The current challenge is to engineer the microenvironment without hampering the spontaneous collective arrangement ability of cells, while simultaneously providing biochemical, geometrical, and biophysical cues that positively influence tissue healing. These efforts have resulted in the development of low-material based TE strategies focused on minimizing the amount of biomaterial provided to the living key players of the regenerative process. Through a "minimalist-engineering" approach, the main idea is to fine-tune the spatial balance occupied by the inanimate region of the regenerative niche toward maximum actuation of the key living components during the healing process.
We aim to assess the learning curve of robotic portal lobectomy with four arms (RPL-4) in patients with pulmonary neoplasms using prospectively collected data.

Data from 100 consecutive cases with lung neoplasms undergoing RPL-4 were prospectively accumulated into a database between June 2018 and August 2019. The Da Vinci Si system was used to perform RPL-4. Regression curves of cumulative sum analysis (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) were fit to identify different phases of the learning curve. Clinical indicators and patient characteristics were compared between different phases.

The mean operative time, console time, and docking time for the entire cohort were 130.6 ± 53.8, 95.5 ± 52.3, and 6.4 ± 3.0 min, respectively. Based on CUSUM analysis of console time, the surgical experience can be divided into three different phases 1-10 cases (learning phase), 11-51 cases (plateau phase), and >51 cases (mastery phase). RA-CUSUM analysis revealed that experience based on 56 cases was required to truly master this technique. Total operative time (p < 0.001), console time (p < 0.001), and docking time (p = 0.026) were reduced as experience increased. However, other indicators were not significantly different among these three phases.

The RPL-4 learning curve can be divided into three phases. Ten cases were required to pass the learning curve, but the mastery of RPL-4 for satisfactory surgical outcomes requires experience with at least 56 cases.
The RPL-4 learning curve can be divided into three phases. Ten cases were required to pass the learning curve, but the mastery of RPL-4 for satisfactory surgical outcomes requires experience with at least 56 cases.
This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).

Demographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine-learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model.

A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC 0.917, accuracy 0.904, recall rate 0.833, and specificity 0.905).

The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision-making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.
The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision-making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.A facile synthesis is reported of two-dimensional (2D) bimetallic (Fe/Co=12) metal-organic frameworks (MOF, ca. 2.2 nm thick) via simple stirring of the reaction mixture of Fe/Co salts and 1,4-benzene dicarboxylic acid (1,4-BDC) in the presence of triethylamine and water at room temperature. The mechanism of the 2D, rather than bulk, MOF was revealed by studying the role of each component in the reaction mixture. It was found that these 2D MOF-Fe/Co(12) exhibited excellent electrocatalytic activity for the oxygen evolution reaction (OER) under basic conditions. The electrocatalytic mechanism was disclosed via both experimental results and density functional theory (DFT) calculation. The 2D morphology and co-doping of Fe/Co contributed to the superior OER performance of the 2D MOF-Fe/Co(12). The simple and efficient synthetic method is suitable for the mass production and future commercialization of functional 2D MOF with low cost and high yield.
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