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Background Postpartum hemorrhage (PPH) is the leading cause of maternal death all over the world. It could be primary or secondary with uterine atony being the cause in 80% of cases. Main body For anticipated PPH, special antenatal preparation for abnormal placentation, inherited coagulation disorders, and Jehovah’s Witnesses must be done. Optimal surgical management of placenta accreta is done by scheduling delivery in an appropriate surgical facility, by insertion of prophylactic embolization catheters in the uterine or internal iliac arteries, and by rapid diagnosis of PPH. The obstetric shock index (SI) is highly specific for PPH. Optimal anesthetic management is done by oxygen supply, using warming devices, ensuring wide bore intravenous (IV) access with adequate volume replacement, and blood product preparation. The non-pneumatic anti-shock garment (NASG) could be used as first-aid compression device. Permissive resuscitation, uterotonic administration, tranexamic acid, recombinant active factor seven (VIIa), and lyophilized fibrinogen concentrate are beneficial. Hemostatic reanimation to correct coagulopathy and cell saver auto-transfusion are applied. For unanticipated PPH, guidelines and regular skill training reduce the incidence of severe PPH. Conclusions Anticipated PPH requires antenatal preparation, optimal anesthetic management with the implementation of permissive resuscitation, hemostatic reanimation, and optimal surgical management.

The novel ligand (H 2 L), N’-(2-cyanoacetyl)isonicotinohydrazide, has been synthesized via reacting the isonicotinic hydrazide with 1-cyanoacetyl-3,5-dimethylpyrazole. The keto-form of the free ligand has been evoked from its spectral data. Based on elemental analyses and mass spectra, the ligand formed 1:1 (M: L) metal complexes with the acetate salts of Cu(II), Co(II), Ni(II) and Zn(II). The complexes’ spectral analyses revealed that the ligand behaved as a mononegative bidentate via the hydrazonyl N 1 and deprotonated enolized acetyl oxygen. Moreover, the DFT quantum chemical calculations revealed that the ligand had higher HOMO and lower LUMO energies than metal complexes, implying an electron donating character. Furthermore, the in vitro anticancer activity against HepG2 and HCT-116 cell lines displayed that the ligand was more potent than doxorubicin against both cell lines, although the metal complexes displayed lower efficacy.

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet; therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance transparency by providing human-understandable insights into their decision-making processes. A broad array of experiments using the LXAIOA-ADPCM technique is performed under the Dementia Prediction dataset. The simulation validation of the LXAIOA-ADPCM technique portrayed a superior accuracy output of 95.71% over existing models.

Biomedical imaging has developed as a non-invasive and effective approach for early disease diagnosis and health monitoring. Diabetes mellitus (DM) is a severe metabolic disease with a high global incidence, characterized by the improper secretion of insulin in the pancreas, which results in elevated blood glucose levels. Moreover, it is one of the most life-threatening illnesses, and a prompt prediction of diabetes is of the highest significance in the present scenario. The analytic models, such as fasting plasma glucose, utilized nowadays are considered to be invasive and time-consuming. So, it is highly essential to develop an easy and non-invasive model for diagnosing DM. For the last few years, several analysis techniques that depend on tongue images have been proposed. The diagnosis of DM is a major subdivision of tongue analysis. Recently, numerous deep learning techniques have been developed and shown to be highly efficient in analyzing DM based on tongue images. This paper presents a Deep Feature Engineering with Crayfish Optimization for Accurate Diabetes Disease Detection via Tongue Image Analysis (DFECO-DDTIA) technique in biomedical imaging. The primary goal of the DFECO-DDTIA technique is to develop an accurate diagnostic method for diabetes using advanced tongue imaging techniques. Initially, the DFECO-DDTIA technique utilizes an upgraded weighted median filtering (Up-WMF) method for noise removal, thereby enhancing image quality. For the feature extraction process, the squeeze-and-excitation-DenseNet (SE-DenseNet) method is employed. Furthermore, the DFECO-DDTIA approach implements the temporal convolutional network (TCN) method for classification. To further optimize the model’s performance, the Crayfish Optimisation Algorithm (COA) method is employed for hyperparameter tuning, ensuring the selection of optimal parameters to enhance accuracy. To highlight the improved performance of the DFECO-DDTIA approach, a comprehensive experimental analysis is conducted under the Tongue images dataset. The comparison analysis of the DFECO-DDTIA approach revealed a superior accuracy value of 96.91% compared to existing models.

Festivals and city-wide mass events are prevalent in human societies worldwide, drawing large crowds. Such events range from concerts with a dozen attendees to large-scale actions with thousands of viewers. It is the highest priority for each organizer of such an occasion to be capable of upholding a higher standard of safety and minimizing the danger of events, especially medical emergencies. Therefore, establishing sufficient safety measures is significant. There is a requirement for event organizers and emergency response personnel to identify developing, potentially critical crowd situations at an early stage during city-wide mass assemblies. In general, the localization of the global positioning system (GPS) and proximity-based tracking is employed to capture intricate crowd dynamics throughout an event. Recently, technology has been used in numerous diverse ways to achieve these large crowds. For example, computer vision-based models are employed to observe the flexibility and behaviour of crowds. In this manuscript, a model for Medical Response Efficiency in Real-Time Large Crowd Environments via Smart Coverage and Hiking Optimisation (MRELC-SCHO) is presented, aiming to maintain stable ecological health. The primary objective of this paper is to propose an effective method for enhancing medical response efficiency in large crowd environments by utilizing advanced optimization algorithms. Initially, the MRELC-SCHO model utilizes min-max normalization to transform the input data into a structured format. Furthermore, the Chimp Optimisation Algorithm (CHOA) model is employed for the feature selection (FS) process to select the most significant features from the dataset. Additionally, the MRELC-SCHO technique utilizes the bidirectional long short-term memory with an auto-encoder (BiLSTM-AE) method for classification. Finally, the parameter selection for the BiLSTM-AE model is performed by using the Hiking Optimisation Algorithm (HOA) model. The experimentation of the MRELC-SCHO approach is accomplished under the Ecological Health dataset. The comparison analysis of the MRELC-SCHO approach revealed a superior accuracy value of 98.56% compared to existing models.

Background Beet filter cake (BFC) is a by-product of sugar beet processing, which is difficult to dispose of and involves severe environmental concerns. Spirulina platensis is a microalga with a high protein content essential for human and animal nutrition. The present study aimed to utilize the beet filter cake extract (BFCE) to produce Spirulina platensis commercially . However, the cultivation of S. platensis on BFCE to produce economically single-cell protein has not been reported previously. Results The batch experiment revealed the maximum dry weight at Zarrouk’s medium (0.4 g/L) followed by 0.34 g/L in the treatment of 75% BFCE. The highest protein content was 50% in Zarrouk’s medium, followed by 46.5% in 25% BFCE. However, adding a higher concentration of 100% BFCE led to a protein content of 31.1%. In the adaption experiment, S platensis showed an increase in dry cell weight and protein content from 25 to 75% BFCE (0.69 g/L to 1.12 g/L and 47.0% to 52.54%, respectively) with an insignificant variation compared to Zarrouk’s medium (p ≤ 0.05), indicating that S. platensis can be economically produced when cultivated on 75% BFCE The predicated parameters from response surface methodology were NaNO 3 (2.5 g/L), NaHCO 3 (0.67 g/L), BFCE (33%) and pH = 8, which resulted in biomass yield and protein content (0.56 g/L and 52.5%, respectively) closer to that achieved using the standard Zarrouk’s medium (0.6 g/L and 55.11%). Moreover, the total essential amino acid content was slightly higher in the optimized medium (38.73%) than SZM (36.98%). Conclusions Therefore, BFCE supplemented medium could be used as a novel low-cost alternative growth medium for producing a single-cell protein with acceptable quantity and quality compared to the standard Zarrouk’s medium. Graphical Abstract

In the human body, the skin is the main organ. Nearly 30–70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermoscopy, which permits surveillance of the hidden structures of skin injuries, i.e., an area suffering from an illness whose effects are unseen to the naked eye. Dermoscopy is generally employed for cancers and other kinds of skin cancers with pigment. Yet, access to a dermoscopy is demanding in resource-poor areas and unnecessary for many general skin diseases. So, developing an effective skin disease analysis method that depends upon effortlessly accessible clinical imaging would be helpful and deliver lower-cost, common access to many individuals. Recently, computer-aided diagnosis (CAD) approaches have been effectively employed to detect skin cancers in dermatoscopic imaging. The CAD-based techniques will be beneficial for helping professionals detect and classify skin lesions. This paper presents an Advanced Skin Lesion Classification using Block-Scrambling-Based Encryption with a Fusion of Transfer Learning Models and a Hippopotamus Optimization (SLCBSBE-FTLHO) model. The main aim of the SLCBSBE-FTLHO model relies on automating the diagnostic procedures of skin lesions using optimal DL approaches. At first, the block-scrambling-based encryption (BSBE) technique is utilized in the image encryption pre-processing stage, and then the decryption process is performed. The feature extraction process employs the fusion of MobileNetV2, GoogLeNet, and AlexNet techniques. Furthermore, the conditional variational autoencoder (CVAE) method is implemented for skin lesion classification. To optimize the CVAE model performance, the hippopotamus optimization (HO) model is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. To exhibit the improved performance of the SLCBSBE-FTLHO approach, a comprehensive experimental analysis is conducted under the skin cancer ISIC dataset. The comparative study of the SLCBSBE-FTLHO approach portrayed a superior accuracy value of 99.48% over existing models.

Background Beet filter cake (BFC) is a food-grade solid waste produced by the sugar industry, constituting a permanent source of pollution. Cyanobacteria are considered a sustainable resource for various bioactive compounds such as phycocyanin pigment with valuable applications. This study aimed to use beet filter cake extract (BFCE) as an alternative medium for the economic cultivation of cyanobacterium Leptolyngbya sp. SSI24 PP723083, then biorefined the bioactive component such as phycocyanin pigment that could be used in the production of selenium nanoparticles. Results The results of the batch experiment displayed that the highest protein content was in BG11medium (47.9%); however, the maximum carbohydrate and lipid content were in 25% BFCE (15.25 and 10.23%, respectively). In addition, 75% BFCE medium stimulated the phycocyanin content (25.29 mg/g) with an insignificant variation compared to BG11 (22.8 mg/g). Moreover, crude phycocyanin extract from Leptolyngbya sp SSI24 cultivated on BG11 and 75% BFCE successfully produced spherical-shaped selenium nanoparticles (Se-NPs) with mean sizes of 95 and 96 nm in both extracts, respectively. Moreover, XRD results demonstrated that the biosynthesized Se-NPs have a crystalline nature. In addition, the Zeta potential of the biosynthesized Se-NPs equals − 17 mV and − 15.03 mV in the control and 75% BFCE treatment, respectively, indicating their stability. The biosynthesized Se-NPs exhibited higher effectiveness against Gram-positive bacteria than Gram-negative bacteria. Moreover, the biosynthesized Se-NPs from BG11 had higher antioxidant activity with IC 50 of 60 ± 0.7 compared to 75% BFCE medium. Further, Se-NPs biosynthesized from phycocyanin extracted from Leptolyngbya sp cultivated on 75% BFCE exhibited strong anticancer activity with IC 50 of 17.31 ± 0.63 µg/ml against the human breast cancer cell line. Conclusions The BFCE-supplemented medium can be used for the cultivation of cyanobacterial strain for the phycocyanin accumulation that is used for the green synthesis of selenium nanoparticles that have biological applications. Graphical Abstract

Background The aim of this study was to assess the effect of intra-operative intra-venous (IV) lidocaine infusion compared to IV morphine, on the post-operative pain at rest, the intra-operative and post-operative morphine requirements, the sedation and the Modified Aldrete scores in the post-anesthesia care unit (PACU), the hemodynamic parameters; mean values of the mean blood pressure (MBP) and the heart rate (HR), the peri-operative changes in the SpO 2 , and the respiratory rate (RR) in laparoscopic Roux-en-y gastric bypass. Sixty patients ˃ 18 years old, with body mass index (BMI) ˃ 35 kg/m 2 , American Society of Anesthesiologists (ASA) physical status II or III, were randomly divided into 2 groups: the lidocaine (L) group patients received intra-operative IV lidocaine infusion, and the morphine (M) group patients received intra-operative IV morphine. Results The post-operative numeric pain rating scale (NPRS) at rest was statistically significant less in group L than in group M patients, in the post-operative 90 min in the PACU. This was reflected on the post-operative morphine requirements in the PACU, as 26.6% of patients in group M required morphine with a mean total dose of 10.8 mg. The mean values of the MBP and HR recorded after intubation were comparable between patients of both groups, indicating attenuation of the stress response to endotracheal intubation by both lidocaine and morphine. However, the mean values of the MBP and HR recorded after extubation were statistically significant lower in patients of group L, indicating the attenuation of the stress response to extubation by lidocaine. Patients in group M showed statistically significant lower mean values of the MBP; before pneumoperitoneum and after 15 min from the pneumoperitoneum, this was reflected on statistically significant higher mean values of the HR. Patients in group L showed statistically significant lower mean values of the MBP and the HR; at 30 and 45 min from the pneumoperitoneum. Patients in group L showed statistically significant lower mean values of the MBP; 60 min from the pneumoperitoneum, after release of pneumoperitoneum and in the PACU. Patients of both groups showed comparable mean values of the HR after 60 min from the pneumoperitoneum, after release of the pneumoperitoneum and in the PACU. No patient in either groups developed post-operative respiratory depression in the PACU. Patients in group L showed statistically significant higher median sedation score, which was reflected on statistically but not clinically significant less Modified Aldrete score in patients of group L. Conclusions In morbid obese patients, the intra-operative IV lidocaine infusion offered post-operative analgesia in the PACU, on the expense of a higher sedation score, which didn’t affect the Modified Aldrete score clinically, with attenuation of the stress response to endotracheal intubation and extubation. Trial registrations FMASU R16/2021. Registered 1st February 2021, with Clinical Trials Registry (NCT05150756) on 10/08/2021.