Limited Medical Facilities Research Articles

Three Delays Model in the Context of Maternal Mortality in Longido District, Tanzania

This study dealt with the three delays model in the context of maternal mortality in Longido District, Tanzania, using the descriptive correlational design. The study selected the district due to the highest maternal mortality rates and number of women giving birth to traditional birth attendants compared to other districts in Arusha. A sample of 311 respondents from 14 health facilities participated by filling out a questionnaire. Data analysis took place through descriptive statistics and regression analysis. Based on the findings, the study concluded that although respondents possessed essential knowledge to prevent maternal mortality, they also possessed negative opinions that could delay their actions against the maternal mortality. While women’s readiness to reach the healthcare facilities prevailed, male partners’ willingness to escort their wives to the healthcare facilities did not exist. Therefore, male-female partnerships in reaching healthcare support emerged as a challenge. Furthermore, the care received from medical facilities sounds unsatisfactory due to limited medical personnel, facilities, and equipment. Social cultural practices predicted the maternal mortality. Therefore, strengthening community awareness, addressing socio-cultural barriers and positivity in accessing services will alleviate delays and reduce maternal mortality. Enhancing family and spousal support towards decision-making and financial support will overcome barriers to care, improve maternal health and reduce the maternal mortality. Strengthening the health system by sustaining resources, accountability, and improved working environment are essential steps towards enhancing the quality of care and consequently reducing maternal mortality.

Survival of hospitalised COVID-19 patients in Hawassa, Ethiopia: a cohort study

The COVID-19 pandemic, caused by SARS-CoV-2, led to 622,119,701 reported cases and 6,546,118 deaths. Most studies on COVID-19 patients in hospitals are from high-income countries, lacking data for developing countries such as Ethiopia.This study assesses clinical features, demographics, and risk factors for in-hospital mortality in Hawassa, Ethiopia. The research cohort comprises 804 cases exhibiting clinical diagnoses and/or radiological findings and indicative of symptoms consistent with COVID-19 at Hawassa University Comprehensive Specialized Hospital from September 24, 2020, to November 26, 2021. In-hospital mortality rate was predicted using Cox regression. The median age was 45 years, with males making up 64.1% of the population. 173 (21.5%) fatalities occurred, with 125 (72.3%) among males. Male patients had higher mortality rates than females. Severe and critical cases were 24% and 21%. 49.1% had at least one comorbidity, with 12.6% having multiple. Common comorbidities were diabetes (15.9%) and hypertension (15.2%). The Cox regression in Ethiopian COVID-19 patients found that factors like gender, advanced age group, disease severity, symptoms upon admission, shortness of breath, sore throat, body weakness, hypertension, diabetes, multiple comorbidities, and prior health facility visits increased the risk of COVID-19 death, similar to high-income nations. However, in Ethiopia, COVID-19 patients were young and economically active. Patients with at least one symptom had reduced death risk. As a conclusion, COVID-19 in Ethiopia mainly affected the younger demographic, particularly economically active individuals. Early detection can reduce the risk of mortality. Prompt medical attention is essential, especially for individuals with comorbidities. Further research needed on diabetes and hypertension management to reduce mortality risk. Risk factors identified at admission play a crucial role in guiding clinical decisions for intensive monitoring and treatment. Broader risk indicators help prioritize patients for allocation of hospital resources, especially in regions with limited medical facilities. Government’s focus on timely testing and strict adherence to regulations crucial for reducing economic impact.

Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

Potential diagnostic application of a novel deep learning- based approach for COVID-19

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID. The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients’ CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

Seeing cells without a lens: Compact 3D digital lensless holographic microscopy for wide-field imaging

Optical microscopy is an essential tool for biomedical discoveries and cell diagnosis at micro- to nano-scales. However, conventional microscopes rely on lenses to record 2-D images of samples, which limits in-depth inspection of large volumes of cells. This research project implements a novel 3-D lensless microscopic imaging system that achieves a wide field of view, high resolution, and an extremely compact, cost-effective design: the Digital Lensless Holographic Microscope (DLHM).A lensless holographic microscope is built with only a light source, a sample, and an imaging chip (with other non-essential supporting structures). The entire setup costs $500 to $600. A series of MATLAB-based algorithms were designed to reconstruct phase information of samples simultaneously from the recorded hologram with built-in high-resolution and phase unwrapping functions. This produces 3-D images of cell samples. The 3-D cell reconstruction of biological samples maintained a comparable resolution with conventional optical microscopes while covering a field of view of 36.2 mm2, which is 20-30 times larger. While most microscopes are extremely time-consuming and require professional expertise, the lensless holographic microscope is portable, low-cost, high-stability, and extremely simple. This makes it accessible for point-of-care testing (POCT) to a broader coverage, including developing regions with limited medical facilities.

A COVID-19 vaccination program for high-risk children aged 12-17 years in Curacao.

This study aimed to describe and critically evaluate the COVID-19 vaccination program for high-risk children in Curacao and provide information about important factors such as parents' vaccination hesitancy and effective strategies for communicating and delivering information about vaccination. This was a cross-sectional study. It was important to identify children aged 12-17 years who were at high risk of severe COVID-19 infection because of the limited medical facilities on the island; children considered to be at high risk were those with diseases such as obesity, hypertension or diabetes mellitus type 2. These children or their caregivers were invited by their pediatricians to be vaccinated as part of a program run by the Public Health Department of Curacao. These high-risk patients were vaccinated between 30 May 2021 and 25 February 2022 in designated child-friendly spaces, with a pediatrician present for guidance and reassurance. Children received the Pfizer-BioNTech COVID-19 vaccine at the recommended dose for their age. The primary outcome was a description and evaluation of the attendance for vaccination. The secondary outcomes were side effects after vaccination for the age groups 12-15 years and 16-17 years. Reasons for refusal or nonadherence were also registered. Altogether 51% (24/47) of those aged 16-17 years who were invited were vaccinated compared with 42% (26/69) of those aged 12-15 years who were invited. Altogether, 46% of these high-risk children were vaccinated compared with 48% of children aged 12-17 years without risk factors. In our population, most patients did not experience any side effects and if they did, the side effects were mild. No cases of myocarditis or pericarditis were observed. A lack of trust in the vaccine and a lack of prioritization of vaccination when scheduling daily activities were important factors in refusal and nonadherence. To organize a successful vaccination program in a small community with limited resources for treating high-risk children it is crucial for medical professionals to provide reliable information. Public health initiatives should focus on assuaging parents' fears about vaccines. In addition, ensuring there is good cooperation between doctors and the Public Health Department can help to make implementation successful. Finally, involving pediatricians and using dedicated areas for vaccinating children can help build trust with parents and caregivers.

Towards The Future - Telerehabilitation In Stroke Care In Pakistanss.

Stroke is the third leading cause of death globally. Pakistan, a low-middle income country (LMIC), reports approximately 350 000 new stroke cases annually, including 20 000 cases each year from Karachi, Pakistan’s largest city populated by 12 million people1. Telerehabilitation is a fairly new branch of telemedicine, defined as the provision of rehabilitation amenities by experts to the patient using telecommunication technology. It reduces hospital stay and expense, thus proving beneficial for both the patient and healthcare provider. Its use is more common in high-income countries (HICs) than LMICs. Popular systems like the Rehab@Home2 framework are examples of telerehabilitation models for patients suffering from stroke3. There is a shortage of literature to comprehensively comment on the impact of telerehabilitation on stroke patients in LMICs. However, telehealth in neurology, termed teleneurology, has the potential for advancement in the treatment of patients with neurological diseases in LMICs, as they have the highest incidence of neurological disorders. A paper proposed that establishing a teleneurology network for Sub-Saharan Africa consisting of transcontinental, inter-regional, intraregional, and national networks will allow neurologists and neurology residents from LMICs to consult with their counterparts in HICs, thus improving the overall quality of the healthcare system4. Pakistan only has 3.2% of the current GDP devoted to the health sector, leading to limited medical facilities nationwide. The outbreak of Covid led to an uplift in the growth of telehealth which encourages evolution in the future. However, this advancement in the use of telehealth has been hindered by several factors since its inception in 2003, including a lack of legal framework and laws for the establishment of new programs, lack of sustainable support from the government, deficiency of funds and resources for the organization of infrastructure, distant rural areas with poverty, low literacy rate, limited cell phone ownership, and internet access. The scarcity of qualified doctors and trained paramedics or nurses and lack of awareness is another significant impediment as depicted by a survey, which informed that 98.2% of doctors employed in the public health sector in Karachi recorded a paucity of training workshops or conferences for telemedicine5. Hence, measures to overcome these barriers like investments from HICs, training workshops from pioneers of the field, and healthcare professionals who can offer services in multiple languages are some of the factors that can aid in effectively introducing telerehabilitation for the masses.

Point-of-care detection of Neisseria gonorrhoeae based on RPA-CRISPR/Cas12a

Gonorrhea, caused by Neisseria gonorrhoeae (N. gonorrhoeae), is a persistent global public health threat. The development of low-cost, point-of-care testing is crucial for gonorrhea control, especially in regions with limited medical facilities. In this study, we integrated CRISPR/Cas12a reaction with recombinase polymerase amplification (RPA) to provide a simple and adaptable molecular detection method for N. gonorrhoeae. The RPA-Cas12a-based detection system developed in this study enables rapid detection of N. gonorrhoeae within 1 h without the use of specialized equipment. This method is highly specific for identifying N. gonorrhoeae without cross-reactivity with other prevalent pathogens. Furthermore, in the evaluation of 24 clinical samples, the detection system demonstrates a 100% concordance rate with traditional culture, which is being used clinically as a reference method. Overall, the RPA-Cas12a-based N. gonorrhoeae detection has the advantages of rapidity, portability, low-cost, no special equipment required, and strong operability, and has a high potential for application as a self-testing and point-of-care diagnosis, which is critical for the clinical management of gonorrhea in developing countries lacking medical equipment.Graphical

An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022.

During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on . We estimate using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.

Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets

It goes without saying that coronavirus (COVID-19) is an infectious disease and many countries are coping with its different variants. Owing to the limited medical facilities, vaccine and medical experts, need of the hour is to intelligently tackle its spread by making artificial intelligence (AI) based smart decisions for COVID-19 suspects who develop different symptoms and they are kept under observation and monitored to see the severity of the symptoms. The target of this study is to analyze COVID-19 suspects data and detect whether a suspect is a COVID-19 patient or not, and if yes, then to what extent, so that a suitable decision can be made. The decision can be categorized such that an infected person can be isolated or quarantined at home or at a facilitation center or the person can be sent to the hospital for the treatment. This target is achieved by designing a mathematical model of COVID-19 suspects in the form of a multi-criteria decision making (MCDM) model and a novel AI based technique is devised and implemented with the help of newly developed plithogenic distance and similarity measures in fuzzy environment. All findings are depicted graphically for a clear understanding and to provide an insight of the necessity and effectiveness of the proposed method. The concept and results of the proposed technique make it suitable for implementation in machine learning, deep learning, pattern recognition etc.

Changes in SpO2 on Room Air for 34 Severe COVID-19 Patients after Ivermectin-Based Combination Treatment: 62% Normalization within 24 Hours

The emergence of COVID-19 in March 2020 challenged Zimbabwe to respond with limited medical facilities and therapeutic options. Based on early clinical indications of efficacy for the macrocyclic lactone, Ivermectin (IVM), against COVID-19, IVM-based combination treatments were deployed to treat it. Oxygen saturation (SpO2) data were retrospectively analyzed for 34 severe, hypoxic COVID-19 patients all on room air (without supplemental oxygen). The patients, median age 56.5, were treated at clinics or at home between August 2020 and May 2021. All but three of these 34 patients had significantly increased SpO2 values within 24 h after the first IVM dose. The mean increase in SpO2 as a percentage of full normalization to SpO2 = 97 was 55.1% at +12 h and 62.3% at +24 h after the first IVM dose (paired t-test, p < 0.0000001). These results parallel similar sharp, rapid increases in SpO2, all on room air, for 24 mostly severe COVID-19 patients in the USA (California) who were given an IVM-based combination treatment. All patients in both of these critical series recovered. These rapid increases in SpO2 values after IVM treatment stand in sharp contrast to declines in SpO2 and associated pulmonary function through the second week following the onset of moderate or severe COVID-19 symptoms under standard care.

Risk Assessment of Hepatitis B Patients of Northern Areas of Pakistan: A case control study

Background: Hepatitis B (HBV) is a common chronic viral disease that affects liver. About 7% people worldwide are infected with HBV with the majority of the cases seen in developing countries with limited medical facilities. It is a blood-borne disease and contributing factors for risk assessment varies from place to place. Aim: To acquire key factors for risk assessment of hepatitis B patients of the northern area of Pakistan. Methods: A case-control study was done among the patients visiting Ayub Teaching Hospital for a regular checkup. The sample included 80 cases and 80 controls by the non-probability convenient method. Data was collected using a structured questionnaire and analyzed by SPSS 23. Frequencies, mean, standard deviation, p values and odds ratio were calculated. Results: A total of 160 subjects were included in this study(80 controls & 80 cases of HBV. Mean age for cases was 38.74±13.597 years. Odds ratios and p values were calculated for different factors. The odds ratio for a history of blood transfusion, sharing toothbrushes, eliciting injectable drugs, history of recent dental procedure and surgical procedure were 1.581, 1.116, 2.206, 1.581 and 1.165 respectively. Conclusion: Although not significant, yet, low socioeconomic status, family history and piercing ears/nose and tattoos resulted in a higher percentage ascontributing factors of HBV infection. In Northern areas of Pakistan, these factors for risk assessment of hepatitis B should be kept in mind and proper disease awareness and primary prevention program should be observed. Keywords: Hepatitis B virus (HBV), risk assessment, Northern areas, KPK, Pakistan.

Conscience and Controversy

Conscience and Controversy

Maritime Telemedicine: Design and Development of an Advanced Healthcare System Called Marine Doctor.

Background: The availability of better healthcare services is critical for onboard seafarers. The development of expert systems can help ships with limited medical facilities, which allow the shipside doctors to properly refer symptoms to remote doctors. This allows clinicians to make a correct diagnosis from there, which leads to proper treatment. A software named Marine Doctor (M Doc) has been developed by incorporating computing technologies to address this objective. Methods: With the help of Information and Communication Technology (ICT) this application can support the provision of appropriate medical assistance to seafarers. The system was developed with Python Tkinter (frontend) and PHP (backend) languages. MySQL was used as a server database. Results: Seafarers can use M Doc to benefit from medical advice that can reduce complications due to misdiagnosis and help doctors to make better-informed decisions. By automatically collecting appropriate sequences of symptoms, doctors will be able to generate proper information for referral of patient symptoms and subsequent advice based on the data. Conclusions: Technology that supports experts on board ships in better interacting with Telemedical Maritime Assistance Services (TMAS) could define the future of medical assistance at sea.

Impact of post-COVID-19 on the hospitality tourism

Within few weeks, when the first reported COVID-19 case in the city of Wuhan, China, the pandemic has spread quickly across nations and tourism has been regarded as the main case in the spread of corona virus worldwide. With no vaccine and limited medical facilities, it plunged the economy into various economic crisis. Both the purchase and production has come to an end due to factory closures; travel bans and border closures. The most severe disruption of the global economy is the national quarantines and unprecedent global restrictions on traveling, bringing international travel to a standstill. As a consequence, tourism operations of many countries have been ceased in March, 2020. According to UNWTO research, 100% UNWTO countries have imposed restrictions on travelling in response to the pandemic and international tourism has completely been closed and the borders were sealed by 72% nations. Due to the cancellation of business trips, holidays, and weekend gateways, tourism industry is heavily impacted by the COVID-19 crisis. So, in this paper we have discussed the impact evaluation of COVID-19 pandemic on tourism industry and its branches. We have reviewed research papers published in 2020, OECD and UNWTO reports.

Breast Thermography as an Adjunct Tool to Monitor the Chemotherapy Response in a Triple Negative BIRADS V Cancer Patient: A Case Study.

Prior studies have reported that breast thermography is a potential adjunct tool to mammography in early cancer detection, especially in developing countries with limited medical facilities. This non-invasive, safe, and painless screening tool can reduce the mortality due to cancer by early detection and monitoring. This prospective study aims to analyze changes in static breast thermograms of a BIRADS V category breast cancer patient to assess the response to Neoadjuvant chemotherapy (NACT) in locally advanced cancer and to compare with thermograms of a BIRADS II category benign patient. Breast thermograms of the malignant and benign patients in five different views were taken using FLIR E40 thermal camera under strict acquisition protocols. Details of the patient along with the thermograms were recorded pre and post NACT. There is a qualitative reduction in the warm region of the surface after the first cycle of chemotherapy treatment. Thermal, fractal, and statistical analysis of thermograms is performed for both patients. In the patient with aggressive ductal carcinoma, the difference in the mean surface temperature between contralateral breasts is high, which is reduced after the first cycle of NACT. This preliminary study indicates that breast thermography can potentially be used as an effective non-invasive, non-contact, and radiation-free tool to analyze the effect of NACT on patients with different stages of breast cancer. This study also signifies the role of the thermography technique in reaching a largely rural population with limited medical resources for early cancer screening.

Portable health clinic COVID-19 system for remote patient follow-up ensuring clinical safety

BackgroundA developing country like Bangladesh suffers very much from the sudden appearance of the COVID-19 pandemic due to the shortage of medical facilities for testing and follow-up treatment. The Portable Health Clinic (PHC) system has developed the COVID-19 module with a triage system for the detection of COVID-19 suspects and the follow-up of the home quarantined COVID-19 patients to reduce the workload of the limited medical facilities. MethodsThe PHC COVID-19 system maintains a questionnaire-based triage function using the experience of the Japanese practice of diseases management for early detection of suspected COVID-19 patients who may need a confirmation test. Then only the highly suspected patients go for testing preventing the unnecessary crowd from the confirmation PCR test centers and hospitals. Like the basic PHC system, it also has the features for patients’ treatment and follow-up for the home quarantined COVID-19 positive and suspect patients using a telemedicine system. This COVID-19 system service box contains 4 self-checking medical sensors, namely, (1) thermometer, (2) pulse oximeter, (3) blood pressure machine, and (4) glucometer for patient's health monitoring including a tablet PC installed with COVID-19 system application for communication between patient and doctor for tele-consultancy. ResultsThis study conducted a COVID-19 triage among 300 villagers and identified 220 green, 45 light-yellow, 2 yellow, 30 orange, and 3 red patients. Besides the 3 red patients, the call center doctors also referred another 13 patients out of the 30 orange patients to health facilities for PCR tests as suspect COVID-19 positive, and to go under their follow-up. Out of these (3 + 13 =) 16 patients, only 4 went for PCR test and 3 of them had been tested positive. The remaining orange, yellow and light-yellow patients were advised home quarantine under the follow-up of the PHC health workers and got cured in 1–2 weeks. ConclusionsThis system can contribute to the community healthcare system by ensuring quality service to the suspected and 80% or more tested COVID-19 positive patients who are usually in the moderate or mild state and do not need to be hospitalized. The PHC COVID-19 system provides services maintaining social distance for preventing infection and ensuring clinical safety for both the patients and the health workers.

Ultra-Early Cord Decompression in the Emergency Setting and its Impact on Outcome

Background Traumatic cervical are injuries are very common due to high motility of cervical spine and its vulnerability to traumatic injuries. Optimal time for stabilizing the patients with traumatic spinal fractures remains controversial. It is almost due to different outcomes in various studies and the lack of consensus about it. Here we explain an ultra–early cord decompression that led to complete recovery of a patient with severe cervical cord injury. Case Presentation The patient was a 27-year-old gymnast woman with a recent history of spinal cord injury caused by high jumping with head back and neck hyperextension presented within 2-hours of trauma. As a critical case and lack of advanced radiologic equipment, only cervical spinal radiographs were used for decision-making within the first 3 hours of injury to save the patient’s cord function by surgical decompression. Conclusion Many studies have proposed different intervention times for achieving the optimal result; however, we present an ultra-early surgery (within 3-hours of injury), conducted in a context of limited medical facilities. This case revealed an excellent result after 12-months follow-up.

Organic light emitting diode for in vitro antimicrobial photodynamic therapy of Candida strains

Organic light emitting diodes (OLEDs) are very attractive light sources because they are large area emitters, and can in principle be deposited on flexible substrates. These features make them suitable for ambulatory photodynamic therapy (PDT). A few reports of in vitro or in vivo OLED based PDT studies for cancer or microbial inhibition are published but to our best knowledge, none against yeasts. Yeast infections are a significant health risk, especially in low income countries with limited medical facilities. In this work, OLED-based antimicrobial PDT (aPDT), using methylene blue (MB) as photosensitizer (PS), is studied to inactivate opportunistic yeast of four Candida strains of two species: Candida albicans and Candida tropicalis. Before aPDT experiments, fluconazole-resistance was evaluated for all strains, showing that both strains of C. tropicalis were resistant and both strains of C. albicans were sensitive to it. We found that 3 repetitive irradiations work better than a single dose while keeping the total fluence constant, and that this result applies whether or not the strains are resistant to fluconazole.