Radiation has been utilized for a long time for the treatment of cancer patients. However, radiotherapy (RT) has many constraints, among which non-selectivity is the primary one. The implementation of nanoparticles (NPs) with RT not only localizes radiation in targeted tissue but also provides significant tumoricidal effect(s) compared to radiation alone. NPs can be functionalized with both biomolecules and therapeutic agents, and their combination significantly reduces the side effects of RT. NP-based RT destroys cancer cells through multiple mechanisms, including ROS generation, which in turn damages DNA and other cellular organelles, inhibiting of the DNA double-strand damage-repair system, obstructing of the cell cycle, regulating of the tumor microenvironment, and killing of cancer stem cells. Furthermore, such combined treatments overcome radioresistance and drug resistance to chemotherapy. Additionally, NP-based RT in combined treatments have shown synergistic therapeutic benefit(s) and enhanced the therapeutic window. Furthermore, a combination of phototherapy, i.e., photodynamic therapy and photothermal therapy with NP-based RT, not only reduces phototoxicity but also offers excellent therapeutic benefits. Moreover, using NPs with RT has shown promise in cancer treatment and shown excellent therapeutic outcomes in clinical trials. Therefore, extensive research in this field will pave the way toward improved RT in cancer treatment.

Background: Radiotherapy (RT)-induced pelvic insufficiency fractures (PIF) are prevalent in patients with cervical cancer. Inconclusive studies on PIF after cervical irradiation create uncertainty. This review examined PIF after RT in cervical patients, including its pathobiology, likely locations of fractures, incidence, clinical symptoms, and predisposing factors. We further discussed study limitations and therapeutic possibilities of PIF. Methods: The following online resources were searched for relevant articles: Google Scholar and PubMed. The keywords ‘pelvic insufficiency fractures’, ‘cervical carcinoma’ and ‘cervical cancer’, as well as ‘chemoradiotherapy’, ‘chemoradiation’, and ‘radiotherapy’, were some of the terms that were used during the search. Results: Patients with PIF report pelvic pain after radiation treatment for cervical cancer; the incidence of PIF ranges from 1.7 to 45.2%. Evidence also supports that among all patients treated with pelvic radiation, those who experienced pelvic insufficiency fractures invariably had at least one sacral fracture, making it the most frequently fractured bone in the body. Menopausal status, weight, BMI, age, and treatments and diagnosis modalities can influence PIF during radiotherapy. Conclusions: In conclusion, our comparative review of the literature highlights significant heterogeneity in various aspects of PIF following radiation for patients with cervical cancer. This diversity encompasses prevalence rates, associated risk factors, symptoms, severity, diagnosis methods, preventive interventions, and follow-up periods. Such diversity underscores the complexity of PIF in this population and emphasizes the critical need for further research to elucidate optimal management strategies and improve patient outcomes.

A novel, label-free optical sensor based on plasmon induced transparency (PIT) has been designed for the detection of basal cell carcinoma in the terahertz (THz) frequency range. The sensor consists of a thin Indium Antimonide (InSb) layer on top of a silica layer with patterned plasmonic antennas where the biosamples are deposited. To overcome the problem associated with the absorption modes of water molecules in THz, the sensor is designed with resonances at frequencies where light absorption of water molecules is minimal. To enhance the reported sensor’s performance features, parametric sweeps have been conducted on the geometrical attributes of the nanostructure. Due to the strong coupling between radiative and dark plasmonic modes, the sensor has a very high sensitivity of 4.50 THz RIU⁻¹, a Q factor of 112.7, and a figure of merit (FOM) of 43.3. The suggested design is ultracompact and easy to fabricate with the potential to be used in numerous biomedical sensing applications.

BACKGROUND Endometriosis is a gynecological condition that involves the implantation of endometrial tissue outside the uterine cavity. About 1.2 million women are suffering from this disease in Bangladesh. OBJECTIVE The purpose of this study was to explore the risk factors, symptoms, and clinical treatment in Bangladesh METHODS In this cross-sectional study, out of 162 infertile women 82 had endometriosis confirmed with laparoscopy and 80 were included in the control group. All were asked to fill out a questionnaire containing demographics, reproductive, and menstrual status. Comparisons between the two groups were done using an Independent T-test, Chi-square test, and logistic regression model. RESULTS The prevalence of endometriosis was higher with, age, marital status, and BMI (P < 0.05). The most common symptoms were dysmenorrhea, excessive bleeding, cramping etc. Infertility (OR:2.21; %95CI: 1.07–4.53; P = 0.03), thyroid imbalance (OR:3.44; %95CI: 1.47–8.03; P = 0.004), irregular menstruation (OR:5.76; %95CI: 2.12–15.60; P = 0.001), age at menarche (OR:2.54; %95CI: 1.04–6.21; P = 0.04) and abortion (OR:2.75; %95CI: 1.31–5.74; P = 0.007) were associated with endometriosis risk. Endometriosis was diagnosed most frequently by TVS (transvaginal ultrasound) at 41.1%, NSAI (nonsteroidal aromatase inhibitors) at 22.8% was the most commonly utilized medicine, and 13.6% of patients undergo laparoscopy. CONCLUSIONS Endometriosis is a considerable public health issue because it affects many women and is associated with significant morbidity. In this study, we developed a model that can be used to predict the risk of endometriosis in infertile women in Bangladesh CLINICALTRIAL BRAC university Institutional Review Board (IRB) under the IRB number BRACUIRB_220240006

Recorded history shows the long coexistence of humans and animals, suggesting it began much earlier. Despite some beneficial interdependence, many animals carry viral diseases that can spread to humans. These diseases are known as zoonotic diseases. Recent outbreaks of SARS-CoV-2, Monkeypox and swine flu viruses have shown how these viruses can disrupt human life and cause death. Fast and accurate predictions of the host from which the virus spreads can help prevent these diseases from spreading. This work presents BiLSTM-VHP, a lightweight bidirectional long short-term memory (LSTM)-based architecture that can predict the host from the nucleotide sequence of orthohantavirus, rabies lyssavirus, and rotavirus A with high accuracy. The proposed model works with nucleotide sequences of 400 bases in length and achieved a prediction accuracy of 89.62% for orthohantavirus, 96.58% for rotavirus A, and 77.22% for rabies lyssavirus outperforming previous studies. Moreover, performance of the model is assessed using the confusion matrix, F-1 score, precision, recall, microaverage AUC. In addition, we introduce three curated datasets of orthohantavirus, rotavirus A, and rabies lyssavirus containing 8,575, 95,197, and 22,052 nucleotide sequences divided into 9, 12, and 29 host classes, respectively. The codes and dataset are available at https://doi.org/10.17605/OSF.IO/ANFKR