"EXCELES"
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Diabetes mellitus is a chronic disease affecting glucose metabolism. The pathophysiological reactions underpinning the disease can lead to the development of late diabetes complications. The gut microbiota plays important roles in weight regulation and the maintenance of a healthy digestive system. Obesity, diabetes mellitus, diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are all associated with a microbial imbalance in the gut. Modern technical equipment and advanced diagnostic procedures, including xmolecular methods, are commonly used to detect both quantitative and qualitative changes in the gut microbiota. This review summarises collective knowledge on the role of the gut microbiota in both types of diabetes mellitus and their late complications, with a particular focus on diabetic foot syndrome.
- MeSH
- diabetes mellitus * MeSH
- diabetická noha * MeSH
- diabetická retinopatie * MeSH
- diabetické nefropatie * etiologie MeSH
- lidé MeSH
- obezita MeSH
- střevní mikroflóra * MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- MeSH
- akademie a ústavy * organizace a řízení MeSH
- financování organizované MeSH
- interdisciplinární výzkum organizace a řízení trendy MeSH
- lidé MeSH
- podpora zdraví MeSH
- průzkumy a dotazníky MeSH
- ukazatele zdravotního stavu MeSH
- vládní programy MeSH
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- lidé MeSH
- Publikační typ
- novinové články MeSH
- rozhovory MeSH
Genomic alterations and enormous monoclonal immunoglobulin production cause multiple myeloma to heavily depend on proteostasis mechanisms, including protein folding and degradation. These findings support the use of proteasome inhibitors for treating multiple myeloma and mantle cell lymphoma. Myeloma treatment has evolved, especially with the availability of new drugs, such as proteasome inhibitors, into therapeutic strategies for both frontline and relapsed/refractory disease settings. However, proteasome inhibitors are generally not effective enough to cure most patients. Natural resistance and eventual acquired resistance led to relapsed/refractory disease and poor prognosis. Advances in the understanding of cellular proteostasis and the development of innovative drugs that also target other proteostasis network components offer opportunities to exploit the intrinsic vulnerability of myeloma cells. This review outlines recent findings on the molecular mechanisms regulating cellular proteostasis pathways, as well as resistance, sensitivity, and escape strategies developed against proteasome inhibitors and provides a rationale and examples for novel combinations of proteasome inhibitors with FDA-approved drugs and investigational drugs targeting the NRF1 (NFE2L1)-mediated proteasome bounce-back response, redox homeostasis, heat shock response, unfolding protein response, autophagy, and VCP/p97 to increase proteotoxic stress, which can improve the efficacy of antimyeloma therapy based on proteasome inhibitors.
- MeSH
- chemorezistence MeSH
- homeostáze proteinů * účinky léků MeSH
- inhibitory proteasomu * terapeutické užití farmakologie MeSH
- lidé MeSH
- mnohočetný myelom * farmakoterapie metabolismus MeSH
- protinádorové látky * terapeutické užití farmakologie MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
INTRODUCTION: Time series analysis is used by statisticians to make predictions from time-ordered data. This is crucial for planning for the future. The inclusion of little-known forecasting function in ExcelTM has brought this type of analysis within the ability of less mathematically sophisticated individuals, including doctors. There are two main models for time series analysis: ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing. This paper will demonstrate how the ubiquitous Excel facilitates a little-known sophisticated forecasting technique that employs the latter and presents a facilitating spreadsheet. METHODS: Excel's FORECAST.ETS function was invoked with supporting macros. RESULTS: A bespoke spreadsheet was created that would prompt for data to be pasted in columns A and B, formatted as a valid date in A and data in B. After error trapping and a horizon date, the FORECAST.ETS function calculates forecasts with 95% CI and a line graph. The FORECAST.ETS.CONFINT was also invoked using a macro to obtain a 95, 96, 97, 98 and 99% confidence intervals table. DISCUSSION: Forecasting is vital in all fields, including the medical field, for innumerable reasons. Statisticians are capable of far more sophisticated time series analyses and techniques and may use multiple techniques that are beyond the competence of ordinary clinicians. However, the sophisticated Excel tool described in this paper allows simple forecasting by anyone with some knowledge of this ubiquitous software. It is hoped that the spreadsheet included with this paper helps to encourage colleagues to engage with this simple-to-use Excel function.
- MeSH
- lidé MeSH
- předpověď * MeSH
- software MeSH
- statistické modely MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The present study examines, as research questions, which and to what extent psychological and demographic variables significantly predict individual, community, and societal resilience among a sample of Czech Republic adults (N = 1,100) six months after the Russian invasion of Ukraine. The research tools included the following scales: Societal, community, and individual resilience; hope, well-being; morale; distress symptoms; a sense of danger; and perceived threats. The results indicated the following: (a) Correlation analysis shows that resilience is significantly and positively correlated with supporting coping factors and significantly and negatively correlated with suppressing coping factors. (b) A comparison of supporting coping indicators (hope, well-being, and morale) and suppressing coping indicators (distress symptoms, sense of danger, and perceived threats) in the Czech Republic with those variables in Slovakia and Israel indicated that Israel reported higher resilience, higher supporting coping indicators, and lower suppressing coping factors. Three-path analysis among the Czech sample indicated that the best predictor of SR was the level of hope, the best predictor of CR was morale, and the best predictor of IR was the sense of danger. In an attempt to explain these findings in the discussion section, we refer to the background of Czech society and a possible connection to the findings.
- MeSH
- copingové dovednosti MeSH
- dospělí MeSH
- lidé MeSH
- psychická odolnost * MeSH
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- dospělí MeSH
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
- Slovenská republika MeSH
- Ukrajina MeSH
Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.
- MeSH
- databáze faktografické * MeSH
- deep learning MeSH
- lidé středního věku MeSH
- lidé MeSH
- Parkinsonova nemoc * patofyziologie MeSH
- řeč * fyziologie MeSH
- senioři MeSH
- strojové učení MeSH
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- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Colorectal cancer (CRC) ranks as the second most prevalent malignancy globally, highlighting the urgent need for more effective diagnostic and therapeutic strategies, as well as a deeper understanding of its molecular basis. Extensive research has demonstrated that cells actively secrete extracellular vesicles (EVs) to mediate intercellular communication at both proximal and distal sites. In this study, we conducted a comprehensive analysis of the RNA content of small extracellular vesicles (sEVs) secreted into the culture media of five frequently utilised CRC cell lines (RKO, HCT116, HCT15, HT29, and DLD1). RNA sequencing data revealed significant insights into the RNA profiles of these sEVs, identifying nine protein-coding genes and fourteen long non-coding RNA (lncRNA) genes that consistently ranked among the top 30 most abundant across all cell lines. Notably, the genes found in sEVs were highly similar among the cell lines, indicating a conserved molecular signature. Several of these genes have been previously documented in the context of cancer biology, while others represent novel discoveries. These findings provide valuable insights into the molecular cargo of sEVs in CRC, potentially unveiling novel biomarkers and therapeutic targets.
- MeSH
- extracelulární vezikuly * metabolismus genetika MeSH
- HCT116 buňky MeSH
- kolorektální nádory * genetika patologie metabolismus MeSH
- lidé MeSH
- nádorové biomarkery genetika metabolismus MeSH
- nádorové buněčné linie MeSH
- regulace genové exprese u nádorů MeSH
- RNA dlouhá nekódující genetika MeSH
- sekvenční analýza RNA MeSH
- stanovení celkové genové exprese MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Immune checkpoints are critical in modulating immune responses and maintaining self-tolerance. Cancer cells can exploit these mechanisms to evade immune detection, making immune checkpoints attractive targets for cancer therapy. The introduction of immune checkpoint inhibitors (ICIs) has transformed cancer treatment, with monoclonal antibodies targeting CTLA-4, PD-1, and PD-L1 demonstrating clinical success. However, challenges such as immune-related adverse events, primary and acquired resistance, and high treatment costs persist. To address these challenges, it is essential to explore alternative strategies, including small-molecule and peptide-based inhibitors, aptamers, RNA-based therapies, gene-editing technologies, bispecific and multispecific agents, and cell-based therapies. Additionally, innovative approaches such as lysosome-targeting chimeras, proteolysis-targeting chimeras, and N-(2-hydroxypropyl) methacrylamide copolymers are emerging as promising options for enhancing treatment effectiveness. This review highlights significant advancements in the field, focusing on their clinical implications and successes.
