The Impact of Cefepime and Ampicillin/Sulbactam on Preventing Post-Cesarean Surgical Site Infections, Randomized Controlled Trail

Over the previous three decades, the rate of caesarean sections performed worldwide has grown exponentially. In comparison to a vaginal birth, the risk of all postpartum infections is higher with a cesarean section. One of the key factors contributing to maternal morbidity is the development of infectious complications in the surgical site after a caesarean section. The primary goal of the research was to compare the efficiency of using ampicillin/sulbactam (AMS) and cefepime (CEF) to reduce the incidence of surgical site infections (SSI) following caesarean delivery. This prospective randomized study was conducted among 200 pregnant women scheduled for elective cesarean section. They were collected from the Obstetrics and Gynecology department of Beni-Suef University Hospital, and then they were randomly assigned into two groups. Group (A) received cefepime 30 min before and 12 h after cesarean delivery, while group (B) received ampicillin/sulbactam 30 min before and 12 h after cesarean delivery. The groups were matched regarding the baseline women characteristics. Comparing the cefepime to the ampicillin/sulbactam revealed that the cefepime significantly decreased superficial SSI from 27% to 14% (0.023). A significant decrease was observed in deep SSI with cefepime compared to ampicillin/sulbactam from 24% to 13% (p-value 0.045). Interestingly, when the cefepime was compared to the ampicillin/sulbactam, we noted that the incidence of endometritis significantly decreased from 13% to 5% (p = 0.048). A noted decrease in post-operative fever in cefepime as compared to ampicillin/sulbactam from 18% to 13% (p-value = 0.329). Receiving prophylactic cefepime pre- and post-cesarean delivery significantly decreases post-operative wound infection and endometritis.

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.

Green Synthesis of a Carbon Quantum Dots-Based Superhydrophobic Membrane for Efficient Oil/Water Separation

The efficient separation of oil and water is a significant challenge worldwide due to the increasing frequency of industrial oily wastewater. Previous work by our group utilizes biological metal-organic framework-based superhydrophobic (S.P) textile fabric for oil/water separation. However, this system is limited due to the low mechanical stability, so there is a need for producing a more robust S.P membrane for oil/water separation. In this study, we report on the synthesis of carbon quantum dots (CQD) from banana leaves via a hydrothermal process and their application in producing a robust S.P coating on textile fabric for oil/water separation. The CQDs were characterized using various techniques including TEM, XRD, absorbance spectroscopy, and the BET method. The TEM images showed that the CQDs were circular in shape with a size of 4.4 nm, while the XRD micrograph indicated that the CQDs were crystalline in nature. The UV-vis graph showed a peak at a wavelength of 278 nm, suggesting strong absorption in the ultraviolet region. The BET-specific surface area of the prepared CQDs is 845 m2/g, with a pore volume of 0.33 cm3/g, and a mean pore diameter of 1.62 nm. We examined the surface wettability, morphology, composition, oil absorption capacity, oil/water separation performance, flux rate, chemical stability, and mechanical stability of the S.P membrane. Our findings indicate that the developed CQD-based S.P membrane possesses excellent S.P properties, displaying high water contact angles of 163° and low water sliding angles of 1°. The membrane demonstrated superior oil absorption capacity, separation efficiency, and flux rate towards three different oils-petroleum ether, n-hexane, and silicone oil. Petroleum ether has the highest separation efficiency (99.5%), and flux rate (13,500 L m-2 h-1), while silicone oil has the lowest. However, silicone oil has the highest absorption capacity (218.9 g/g) and petroleum ether has the lowest (194.8 g/g). For the absorption capacity and separation efficiency, a one-way ANOVA test was conducted. The statistical analyses revealed significant differences in absorption capacity and separation efficiency for the three oils, highlighting the efficacy of the superhydrophobic membrane for tailored oil/water separation. Additionally, the S.P membrane exhibited good mechanical (the membrane maintains its superhydrophobicity until an abrasion length of 850 cm) and chemical stability (the membrane maintains its superhydrophobicity in pH range 1-13), withstanding abrasion and immersion in solutions of varying pH values. The CQD-based S.P membrane shows great potential as a promising material for oil/water separation applications, with excellent performance and stability under various environmental conditions.

Comprehensive evaluation of zeolite/marine alga nanocomposite in the removal of waste dye from industrial wastewater

A systematic study integrating laboratory, analytical, and case study field trial was conducted to figure out the effective adsorbent that could be used for the removal of Congo red (CR) dye from industrial wastewater effluent. The ability of the zeolite (Z) to adsorb CR dye from aqueous solutions was evaluated after it was modified by the Cystoseira compressa algae (CC) (Egyptian marine algae). Zeolite, CC algae were combined together in order to form the new composite zeolite/algae composite (ZCC) using wet impregnation technique and then characterized by the aid of different techniques. A noticeable enhancement in the adsorption capacity of newly synthesized ZCC was observed if compared to Z and CC, particularly at low CR concentrations. The batch style experiment was selected to figure out the impact of various experimental conditions on the adsorption behavior of different adsorbents. Moreover, isotherms and kinetics were estimated. According to the experimental results, the newly synthesized ZCC composite might be applied optimistically as an adsorbent for eliminating anionic dye molecules from industrial wastewater at low dye concentration. The dye adsorption on Z and ZCC followed the Langmuir isotherm, while that of CC followed the Freundlich isotherm. The dye adsorption kinetics on ZCC, CC, and Z were agreed with Elovich, intra-particle, and pseudo-second-order kinetic models, correspondingly. Adsorption mechanisms were also assessed using Weber's intraparticle diffusion model. Finally, field tests showed that the newly synthesized sorbent has a 98.5% efficient in eliminating dyes from industrial wastewater, authorizing the foundation for a recent eco-friendly adsorbent that facilitate industrial wastewater reuse.