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People with visual impairment face various difficulties in their daily activities in comparison to people without visual impairment. Much research has been done to find smart solutions using mobile devices to help people with visual impairment perform tasks like shopping. One of the most challenging tasks for researchers is to create a solution that offers a good quality of life for people with visual impairment. It is also essential to develop solutions that encourage people with visual impairment to participate in social life. This study provides an overview of the various technologies that have been developed in recent years to assist people with visual impairment in shopping tasks. It gives an introduction to the latest direction in this area, which will help developers to incorporate such solutions into their research.

Pelvic fractures, particularly acetabular fractures, pose major problems for individuals with advanced age due to comorbidities and poor bone quality. Road traffic accidents (RTAs) are a leading cause of high-energy injuries. This case report describes the treatment of an 80-year-old patient with hypertension, pulmonary fibrosis, and morbid obesity who suffered an acetabular fracture after an RTA. An 80-year-old patient was received in the emergency room 10 days after the RTA. X-rays and CT scans indicated an anterior column with a posterior hemi-transverse fracture, and the quadrilateral plate was completely displaced. A CT angiography revealed deep vein thrombosis (DVT) in the lower limb, prompting the start of anticoagulant medication and the insertion of an inferior vena cava (IVC) filter. The modified Stoppa technique was used to definitively fix the acetabular fracture. The corona mortis was found and safeguarded throughout the surgery. Following surgery, the patient avoided weight-bearing activities for one month before beginning non-weight-bearing mobilization and physical therapy. At the first clinic visit following discharge, the patient reported total pain relief and successful mobilization with a wheelchair. The treatment of acetabular fractures in older patients with comorbidities necessitates a specialized, multidisciplinary approach. This case indicates that, despite major hurdles, successful outcomes can be achieved through appropriate surgical and postoperative techniques. Future research should focus on refining these methods to enhance the prognosis in this patient population.

Alzheimer's disease (AD), a significant public health challenge, requires accurate early diagnosis to improve patient outcomes. Vision Transformers (ViTs) and Convolutional Vision Transformers (CViTs) have emerged as powerful Deep Learning architectures for this task. Following PRISMA guidelines, this systematic review analyzes 68 studies selected from 564 publications (2021-2025) across five major databases: Scopus, Web of Science, ScienceDirect, IEEE Xplore, and PubMed. We introduce novel taxonomies to systematically categorize these works by model architecture, data modality, fusion strategy, and diagnostic objective. Our analysis reveals key trends, such as the rise of hybrid CViT frameworks, and critical gaps, including a limited focus on Mild Cognitive Impairment-to-AD progression. Critically, we also assess practical implementation details, revealing widespread challenges in algorithmic reproducibility. The discussion culminates in a forward-looking analysis of Large Vision Models and proposes future directions emphasizing the need for robust multimodal integration, lightweight transformer designs, and Explainable AI to advance AD research and bridge the critical gap between high-performance modeling and clinical applicability.

People with visual impairment face a lot of difficulties in their daily activities. Several researches have been conducted to find smart solutions using mobile devices to help people with visual impairment perform tasks. This paper focuses on using assistive technology to help people with visual impairment in indoor navigation using markers. The essential steps of a typical navigation system are identifying the current location, finding the shortest path to the destination, and navigating safely to the destination using navigation feedback. In this research, the authors proposed a system to help people with visual impairment in indoor navigation using markers. In this system, the authors have re-defined the identification step to a classification problem and used convolutional neural networks to identify markers. The main contributions of this paper are: (1) A system to help people with visual impairment in indoor navigation using markers. (2) Comparing QR codes with Aruco markers to prove that Aruco markers work better. (3) Convolutional neural network has been implemented and simplified to detect the candidate markers in challenging conditions and improve response time. (4) Comparing the proposed model with another model to prove that it gives better accuracy for training and testing.

Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.

Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.

Many crimes are committed every day all over the world, and one of them is a criminal offense that includes a wide range of illegal acts such as murder, theft, assault, rape, kidnapping, fraud, and others. Criminals pose a threat to security, which harms the public interest. In this case, the police question all eyewitnesses at the crime scene, and sometimes, witnesses who were present at the crime scene can remember the face of the criminal. The witness accurately describes the person's facial features in the report, such as eyes, nose, etc. Law enforcement authorities use eyewitness information to identify the person. Criminal investigations can be accelerated by converting sketched faces into actual images, but this requires eyewitnesses to confirm the description in the report. Drawings make it very difficult to identify real human faces because they do not contain the details that help to catch criminals. In contrast, color photographs contain many details that help to identify facial features more clearly. This work proposes to generate color images using the modified modulation Sketch-to-Face CycleGAN and then pass them through Generative Facial Prior-GAN. CycleGAN consists of a generator and discriminator. The generator is used to generate colored images, and the discriminator is used to identify whether the images are real or fake. These are then passed to GFPGAN to improve the quality of the colored images. The structural similarity index measure of 0.8154 is achieved when creating photorealistic images from drawings.

Cell-cell interactions with physiological and morphogenetic consequences (cell-cell recognition) characterize vertebrate development. This is evident in myogenesis, where fusion of individual embryonic myoblasts into muscle fibers is a multi-step sequence: prostaglandin (PG) release within the tissue, binding of released PG to specific cell surface receptors, myoblast \"recognition\" and fusion of the individual myoblast plasma membranes to form myotubes. Two major questions are how are these cell surface events causally connected, and what is the nature of the second messenger that transmits the information to the nucleus to coordinate gene expression during myogenesis. To explore this myogenic signaling pathway, the change in cell-cell adhesion associated with the binding of PG to embryonic chick myoblasts in culture was investigated. $\\rm Li\\sp{+}$ (lithium) blocked or delayed cell-cell adhesion, and addition of inositol to $\\rm Li\\sp{+}$-blocked cultures restored normal cell-cell adhesion. This suggested involvement of an inositol phosphate messenger system. Indomethacin blocks PG synthesis. Addition of indomethacin blocked cell-cell adhesion, and this block was rescued by addition of $\\rm PGE\\sb1$. PG was required only between 30 hours and 37 hours in culture, an interval when myogenesis is also sensitive to $\\rm Li\\sp{+}$. These results are consistent with signaling between embryonic myoblasts involving PG metabolism, activation of a membrane receptor and changes in phosphatidyl inositol metabolism. Since many receptors affect inositol phosphate metabolism by activating a G protein, this mechanism was investigated. Pertussis toxin was added to myoblasts and the change in cell adhesion measured. A pertussis toxin-sensitive step was found to be required before cell-cell adhesion occurred, suggesting that a G protein couples the activated prostaglandin receptor to polyphosphatidyl inositol metabolism. This hypothesis was further tested. Myoblasts were prelabeled with $\\sp3$H-inositol and subsequently $\\rm Li\\sp{+}$ was added. Inositol-1-phosphate (IP) accumulated after 33 hours to 36 hours in culture but not after 37 hours, supporting a role for a polyphosphatidyl inositol (PIP$\\sb2$) second messenger system. Finally, the possibility that PGR activation is critical to cell adhesion was tested. Myoblasts were prelabeled with 3H-inositol and indomethacin was added to prevent PG synthesis. IP accumulation was measured in the presence of $\\rm Li\\sp{+}$ or $\\rm Li\\sp{+}$ plus indomethacin. No IP accumulation was found when indomethacin was added in the presence of $\\rm Li\\sp{+}$. However, addition of $\\rm PGE\\sb1$ caused IP accumulation. These results suggest that a $\\rm PIP\\sb2$ second messenger system connects PGR activation with cell-cell adhesion during myoblast differentiation.

The purpose of this study was to explore the value of using cefepime and ceftazidime in treating patients with COVID-19. A total of 370 (162 males) patients, with RT-PCR-confirmed cases of COVID-19, were included in the study. Out of them, 260 patients were treated with cefepime or ceftazidime, with the addition of steroids to the treatment. Patients were divided into three groups: Group 1: patients treated with cefepime (124 patients); Group 2: patients treated with ceftazidime (136 patients); Group 3 (control group): patients treated according to the WHO guidelines and the Egyptian COVID-19 management protocol (110 patients)/ Each group was classified into three age groups: 18–30, 31–60, and >60 years. The dose of either cefepime or ceftazidime was 1000 mg twice daily for five days. Eight milligrams of dexamethasone were used as the steroidal drug. Careful follow-ups for the patients were carried out. In vitro and in silico Mpro enzyme assays were performed to investigate the antiviral potential of both antibiotics. The mean recovery time for Group 1 was 12 days, for Group 2 was 13 days, and for Group 3 (control) was 19 days. No deaths were recorded, and all patients were recovered without any complications. For Group 1, the recovery time was 10, 12, and 16 days for the age groups 18–30, 30–60, and >60 years, respectively. For Group 2, the recovery time was 11, 13, and 15 days for the age groups 18–30, 30–60, and >60 years, respectively. For Group 3 (control), the recovery time was 15, 16, and 17 days for the age groups 18–30, 30–60, and >60 years, respectively. Both ceftazidime and cefepime showed very good inhibitory activity towards SARS CoV-2′s Mpro, with IC50 values of 1.81 µM and 8.53 µM, respectively. In conclusion, ceftazidime and cefepime are efficient for the management of moderate and severe cases of COVID-19 due to their potential anti-SARS CoV-2 activity and low side effects, and, hence, the currently used complex multidrug treatment protocol can be replaced by the simpler one proposed in this study.

Marjoram ( Origanum majorana L.), also known as “sweet marjoram” or “sweet oregano” is a Mediterranean herbaceous perennial herb cultivated in Egypt and widely consumed as an herbal supplement for treatment of several ailments. The main goal of this study was to assess volatiles’ variation in marjoram samples collected from two different widely consumed commercial products using two different extraction techniques viz. head space solid phase microextraction (HS-SPME) and petroleum ether using gas chromatography mass spectrometry (GC-MS) analysis and multivariate data analysis. A total of 20 major aroma compounds were identified in samples extracted with HS-SPME found enriched in monoterpene hydrocarbons and oxygenated compounds. The major volatiles included β -phellandrene (20.1 and 14.2%), γ -terpinene (13.4 and 11.7%), 2-bornene (12.3 and 11.5%), p -cymene (9.8 and 4.6%) terpenen-4-ol (16.4 and 7.5%), sabinene hydrate (16.02 and 8.8%) and terpineol (4.2 and 3.2%) in MR and MI, respectively. Compared with HS-SPME, 51 aroma compounds were identified in marjoram samples extracted with petroleum ether, found more enriched in aliphatic hydrocarbons (42.8 and 73.8%) in MR and MI, respectively. While a higher identification score was observed in the case of solvent extraction, SPME appeared to be more selective in the recovery of oxygenated terpenes to account more for marjoram aroma. Multivariate data analysis using principal component analysis (PCA) revealed distinct discrimination between volatile composition of both marjoram samples. The total phenolic and flavonoid contents in marjoram samples were at (111.9, 109.1 µg GA/mg) and (18.3, 19.5 µg rutin eq/mg) in MR and MI, respectively. Stronger antioxidant effects were observed in MR and MI samples with IC 50 at 45.5 and 56.8 µg/mL respectively compared to IC 50 6.57 µg/mL for Trolox as assayed using DPPH assay. Moderate anti-bacterial effect was observed in MR and MI samples and expressed as a zone of inhibition mostly against Bacillus subtilis (16.03 and 15.9 mm), B. cereus (12.9 and 13.7 mm), Enterococcus faecalis (14.03 and 13.97 mm), and Enterobacter cloacae (11.6 and 11.6 mm) respectively.