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We did observe that EGFR was significantly associated with increased age of our patients and with worse survival. Age > 40 years was a predictor for worse prognosis as well.

EGFR overexpression and advanced age were worse prognostic indicators.

EGFR overexpression and advanced age were worse prognostic indicators.

Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients.

A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, talized medicine.

This work aims to quantify potential pollution level changes in an urban environment (Madrid city, Spain) located in South Europe due to the lockdown measures for preventing the SARS-CoV-2 transmission. Polluting 11 species commonly monitored in urban zones were attended. Except for O

, a prompt target pollutant levels abatement was reached, intensely when implanted stricter measures and moderately along those measures' relaxing period. In the case of TH and CH

, it is evidenced a progressive diminution over the lockdown period. While the highest decreasing average changes relapsed on NOx (NO

- 40.0% and NO - 33.3%) and VOCs (C

H

- 36.3% and C

H

- 32.8%), followed by SO

(- 27.0%), PM

(- 19.7%), CO (- 16.6%), CH

(- 14.7%), TH (- 11.6%) and PM

(- 10.1%), the O

level slightly raised 0.4%. These changes were consistently dependent on the measurement station location, emphasizing urban background zones for SO

, CO, C

H

, C

H

, TH and CH

, suburban zones for PM

and O

, urban traffic sites for NO and PM

, and keeping variations reasonably similar at all the stations in the case of NO

. Those pollution changes were not translated in variations on geospatial pattern, except for NO, O

and SO

. Although the researched urban atmosphere improvement was not attributable to meteorological conditions' variations, it was in line with the decline in traffic intensity. The evidenced outcomes might offer valuable clues to air quality managers in urban environments regarding decision-making in favor of applying punctual severe measures for quickly and considerably relieving polluting high load occurred in urban environments.

The online version contains supplementary material available at 10.1007/s13762-022-04464-6.

The online version contains supplementary material available at 10.1007/s13762-022-04464-6.Coronavirus Pandemic is the current biggest challenge against humanity. Apart from the personal health issues and higher mortality by the coronavirus, recent research works have also reported the environmental impacts of the pandemic. The review aims to analyze the current status of face masks and personal protective equipment littering and subsequent environmental impact in terms of microplastic and microfiber pollution. Recent researches in this domain are collected from the leading databases with relevant keywords and critically analyzed. The review results report a multi-fold increment in the usage of personal protective equipment, particularly face masks after the pandemic. Mismanagement of these items leads them to reach the marine environment through a variety of transportation. The results show a significant amount of increment in plastic and pandemic-related littering after the pandemic. The systematic review shows that the use of synthetic fibers in disposable personal protective equipment and masks leads to release of fibers that can add-on to microfiber pollution. The results are also true in the case of reusable masks as the repeated laundry and disinfection methods release a significantly higher amount of microfibers. Only very few studies have addressed the release of microfiber from the mask, and no studies have reported the impact of personal protective equipment. The worldwide mass adaptation and improper disposal of these materials increase the seriousness of the problem multiple folds. These findings suggest the immediate requirement of critical analysis of the pandemic-related littering and microfiber release characteristics. The research also urges the need for the implementation of an environmental management plan as a mitigation strategy around the globe.Metaverse, which is anticipated to be the future of the internet, is a 3D virtual world in which users interact via highly customizable computer avatars. It is considerably promising for several industries, including gaming, education, and business. However, it still has drawbacks, particularly in the privacy and identity threads. When a person joins the metaverse via a virtual reality (VR) human-robot equipment, their avatar, digital assets, and private information may be compromised by cybercriminals. This paper introduces a specific Finger Vein Recognition approach for the virtual reality (VR) human-robot equipment of the metaverse of the Metaverse to prevent others from misappropriating it. Finger vein is a is a biometric feature hidden beneath our skin. It is considerably more secure in person verification than other hand-based biometric characteristics such as finger print and palm print since it is difficult to imitate. Most conventional finger vein recognition systems that use hand-crafted features are ineffective, especially for images with low quality, low contrast, scale variation, translation, and rotation. Deep learning methods have been demonstrated to be more successful than traditional methods in computer vision. This paper develops a finger vein recognition system based on a convolution neural network and anti-aliasing technique. We employ/ utilize a contrast image enhancement algorithm in the preprocessing step to improve performance of the system. The proposed approach is evaluated on three publicly available finger vein datasets. Experimental results show that our proposed method outperforms the current state-of-the-art methods, improvement of 97.66% accuracy on FVUSM dataset, 99.94% accuracy on SDUMLA dataset, and 88.19% accuracy on THUFV2 dataset.Two zoonotic protozoan pathogens, Giardia duodenalis and Toxoplasma gondii, are important causes of waterborne infections in the Quindío region in Colombia. No previous data exist on how contamination occurs at the source for drinking water consumed by the human population in this region. Our aim was to describe the frequency of G. duodenalis and T. gondii DNA in 11 sampling points during a five-month period in water and adjacent soil at the Quindío River basin (Andean region in the central western part of Colombia). The study employed nested PCR for T. gondii, using the B1 gene as the amplification target, and single-round PCR for G. duodenalis assemblage A and assemblage B, amplifying the gdh gene, followed by DNA sequencing. In 50 soil samples, 28% (14/50) were positive for T. gondii. For G. duodenalis, distribution was in equal parts for assemblage A (8%; 4/50) and assemblage B (8%, 4/50). Genotyping of T. gondii sequences showed two soil samples with type I strain, another two samples of soil with type III strain, but most samples were of unidentified strains. In water samples, T. gondii was detected in 9.1% (5/55), G. duodenalis assemblage A in 34.5% (19/55), and G. duodenalis assemblage B in 12.7% (7/55). T. gondii DNA positivity was associated with lower soil temperature (p = 0.0239). Presence of G. duodenalis and T. gondii was evidenced in soil and water samples in the Quindío River basin, indicating soil as the potential source of contamination for the river that it is destined for human consumption. Monitoring these protozoa in drinking water is necessary to prevent public health risks in human populations.

SARS-CoV-2 causes severe acute respiratory syndrome prompting worldwide demand for new antiviral treatments and supportive care for organ failure caused by this life-threatening virus. This study aimed to help develop a new Traditional Persian Medicine (TPM) -based drug and assess its efficacy and safety in COVID-19 patients with major symptoms.

In February 2022, a randomized clinical trial was conducted among 160 patients with a confirmed diagnosis of COVID-19 admitted to Emam Reza (AJA) Hospital in Tehran, Iran. During their hospitalization, the intervention group received a treatment protocol approved by Iran's Ministry of Health and Medical Education (MOHME), consisting of an Iranian regimen, Ficus carica; Vitis vinifera, Safflower, Cicer arietinum, Descurainiasophia seeds, Ziziphus jujuba, chicken soup, barley soup, rose water, saffron, and cinnamon spices. All patients were compared in terms of demographics, clinical, and laboratory variables.

One hundred and sixty COVID-19 patients were divided into two groups intervention and control. In baseline characteristics, there was no significant difference between the intervention and control groups (

>0.05). CX-5461 datasheet Using SPSS software version 22, statistical analysis revealed a significant difference in four symptoms myalgia, weakness, headache, and cough (

<0.05). During the 5-day treatment period, the control group had significantly lower C-reactive protein (

<0.05).

While more research with a larger sample size is needed, the proposed combination appears to be effective in the treatment of symptoms as well as inflammatory biomarkers such as C-reactive protein in COVID-19 patients.Iranian registry of clinical trials (IRCT) IRCT20220227054140N1.

While more research with a larger sample size is needed, the proposed combination appears to be effective in the treatment of symptoms as well as inflammatory biomarkers such as C-reactive protein in COVID-19 patients.Iranian registry of clinical trials (IRCT) IRCT20220227054140N1.The Long Island Sound (LIS) Tropospheric Ozone Study was a multi-agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF-CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33-km grid spacing, going from -0.12 to 0.08 K. Additionally, 4-km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution.