Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images.

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

A CMOS Image Sensor Based Refractometer without Spectrometry.

The refractive index (RI), an important optical property of a material, is measured by commercial refractometers in the food, agricultural, chemical, and manufacturing industries. Most of these refractometers must be equipped with a prism for light dispersion, which drastically limits the design and size of the refractometer. Recently, there have been several reports on the development of a surface plasmon resonance (SPR)-based RI detector, which is characterized by its high sensitivity and simplicity. However, regardless of the prism, an expensive spectrometer is required to analyze the resonance wavelength or angle of incidence. This paper proposes a method that eliminates the need for the prism and other conventional spectrometer components. For this purpose, total internal reflection SPR technology was used on an Ag thin film, and RI analysis was combined with a lens-free CMOS image sensor or a smartphone camera. A finite-difference time-domain (FDTD) numerical simulation was performed to evaluate the relationship between the output power intensity and Ag film thickness for different RIs at three wavelengths of commercial light-emitting diodes (LEDs). The maximum sensitivity of -824.54 RIU-1 was achieved with AG20 at an incident wavelength of 559 nm. Due to its simple design and cost effectiveness, this prism-less, SPR-based refractometer combined with a lens-free CMOS image sensor or a smartphone could be a superior candidate for a point-of-care device that can determine the RIs of various analytes in the field of biological or chemical sensing.

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24-84 mm and 8-48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95-91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Detection of Particulate Matters with a Field-Portable Microscope Using Side-Illuminated Total Internal Reflection.

Field-portable observation and analysis of particulate matter (PM) is required to enhance healthy lives. Various types of the PM measurement methods are in use; however, each of these methods has significant limitations in that real time measurement is impossible, the detection system is bulky, or the measurement accuracy is insufficient. In this work, we introduce an optical method to perform a fast and accurate PM analysis with a higher-contrast microscopic image enabled by a side-illuminated total internal reflection (TIR) technique to be implemented in a compact device. The superiority of the proposed method was quantitatively demonstrated by comparing the signal-to-noise ratio of the proposed side-illuminated TIR method with a traditional halogen lamp-based transmission microscope. With the proposed device, signal-to-noise ratios (SNRs) for microbeads (5~20 µm) and ultrafine dust particles (>5 µm) increased 4.5~17 and 4~10 dB, respectively, compared to the conventional transmission microscope. As a proof of concept, the proposed method was also applied to a low-cost commercial smartphone toy microscope enabling field-portable detection of PMs. We believe that the proposed side-illuminated TIR PM detection device holds significant advantages over other commonly used systems due to its sufficient detection capability along with simple and compact configuration as well as low cost.

Investigation on the Printed CNT-Film-Based Electrochemical Sensor for Detection of Liquid Chemicals.

We studied electrochemical sensors using printed carbon nanotubes (CNT) film on a polyethylene telephtalate (PET) substrate. The mechanical stability of the printed CNT film (PCF) was confirmed by using bending and Scotch tape tests. In order to determine the optimum sensor structure, a resistance-type PCF sensor (R-type PCF sensor) and a comb-type PCF sensor (C-type PCF sensor) were fabricated and compared using a diluted NH3 droplet with various concentrations. The magnitude of response, response time, sensitivity, linearity, and limit of detection (LOD) were compared, and it was concluded that C-type PCF sensor has superior performance. In addition, the feasibility of PCF electrochemical sensor was investigated using 12 kinds of hazardous and noxious substances (HNS). The detection mechanism and selectivity of the PCF sensor are discussed.

PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks.

BACKGROUND: How can we obtain fast and high-quality clusters in genome scale bio-networks? Graph clustering is a powerful tool applied on bio-networks to solve various biological problems such as protein complexes detection, disease module detection, and gene function prediction. Especially, MCL (Markov Clustering) has been spotlighted due to its superior performance on bio-networks. MCL, however, is skewed towards finding a large number of very small clusters (size 1-3) and fails to detect many larger clusters (size 10+). To resolve this fragmentation problem, MLR-MCL (Multi-level Regularized MCL) has been developed. MLR-MCL still suffers from the fragmentation and, in cases, unrealistically large clusters are generated. RESULTS: In this paper, we propose PS-MCL (Parallel Shotgun Coarsened MCL), a parallel graph clustering method outperforming MLR-MCL in terms of running time and cluster quality. PS-MCL adopts an efficient coarsening scheme, called SC (Shotgun Coarsening), to improve graph coarsening in MLR-MCL. SC allows merging multiple nodes at a time, which leads to improvement in quality, time and space usage. Also, PS-MCL parallelizes main operations used in MLR-MCL which includes matrix multiplication. CONCLUSIONS: Experiments show that PS-MCL dramatically alleviates the fragmentation problem, and outperforms MLR-MCL in quality and running time. We also show that the running time of PS-MCL is effectively reduced with parallelization.

A Field-Portable Cell Analyzer without a Microscope and Reagents.

This paper demonstrates a commercial-level field-portable lens-free cell analyzer called the NaviCell (No-stain and Automated Versatile Innovative cell analyzer) capable of automatically analyzing cell count and viability without employing an optical microscope and reagents. Based on the lens-free shadow imaging technique, the NaviCell (162 × 135 × 138 mm³ and 1.02 kg) has the advantage of providing analysis results with improved standard deviation between measurement results, owing to its large field of view. Importantly, the cell counting and viability testing can be analyzed without the use of any reagent, thereby simplifying the measurement procedure and reducing potential errors during sample preparation. In this study, the performance of the NaviCell for cell counting and viability testing was demonstrated using 13 and six cell lines, respectively. Based on the results of the hemocytometer (de facto standard), the error rate (ER) and coefficient of variation (CV) of the NaviCell are approximately 3.27 and 2.16 times better than the commercial cell counter, respectively. The cell viability testing of the NaviCell also showed an ER and CV performance improvement of 5.09 and 1.8 times, respectively, demonstrating sufficient potential in the field of cell analysis.

A Human Serum-Based Enzyme-Free Continuous Glucose Monitoring Technique Using a Needle-Type Bio-Layer Interference Sensor.

The incidence of diabetes is continually increasing, and by 2030, it is expected to have increased by 69% and 20% in underdeveloped and developed countries, respectively. Therefore, glucose sensors are likely to remain in high demand in medical device markets. For the current study, we developed a needle-type bio-layer interference (BLI) sensor that can continuously monitor glucose levels. Using dialysis procedures, we were able to obtain hypoglycemic samples from commercial human serum. These dialysis-derived samples, alongside samples of normal human serum were used to evaluate the utility of the sensor for the detection of the clinical interest range of glucose concentrations (70-200 mg/dL), revealing high system performance for a wide glycemic state range (45-500 mg/dL). Reversibility and reproducibility were also tested over a range of time spans. Combined with existing BLI system technology, this sensor holds great promise for use as a wearable online continuous glucose monitoring system for patients in a hospital setting.

Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders.

How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.

Efficacy of direct-to-operating room trauma resuscitation: a systematic review.

BACKGROUND: Hemorrhage control is a time-critical task, and recent studies have demonstrated that a shorter time to definitive care is positively associated with patient survival and functional outcomes. The concept of direct transport to the operating room was proposed in the 1960s to reduce treatment time. Some trauma centers have developed protocols for direct-to-operating room resuscitation (DOR) programs. Moreover, few studies have reported the clinical outcomes of DOR in patients with trauma; however, their clinical effect in improving the efficiency and quality of care remains unclear. In this systematic review, we aimed to consolidate all published studies reporting the effect of DOR on severe trauma and evaluate its utility. METHODS: The PubMed, EMBASE, and Cochrane databases were searched from inception to April 2023, to identify all articles published in English that reported the effect of direct-to-operating room trauma resuscitation for severe trauma. The articles were reviewed as references of interest. RESULTS: We reviewed six studies reporting the clinical effect of operating room trauma resuscitation. A total of 3232 patients were identified. Five studies compared the actual mortality with the predicted mortality using the trauma score and injury severity score, while one study compared mortality using propensity matching. Four studies reported that the actual survival rate for overall injuries was better than the predicted survival rate, whereas two studies reported no difference. Some studies performed subgroup analyses. Two studies showed that the survival rate for penetrating injuries was better than the predicted survival rate, and one showed that the survival rate for blunt injuries was better than the predicted survival rate. Five studies reported the time to surgical intervention, which was within 30 min. Two studies time-compared surgical intervention, which was shorter in patients who underwent DOR. CONCLUSION: Implementing DOR is likely to have a beneficial effect on mortality and can facilitate rapid intervention in patients with severe shock. Future studies, possibly clinical trials, are needed to ensure a proper comparison of the efficiency.

A quantitative analysis of surgical smoke-derived particulate matter and formaldehyde exposure during spine surgery: a possible occupational hazard.

OBJECTIVE: Since its introduction, electrocautery has served as a valuable surgical tool, enabling precise tissue cutting and effective hemostasis in spine surgery. While there have been numerous efforts to elucidate the possible hazardous effects of surgical smoke in various surgical fields, there has been very little discussion in the context of spine surgery. The objective of this study was to measure and conduct a quantitative analysis of the particulate matter (PM) of different sizes and of formaldehyde (HCHO) generated by smoke during spine surgeries. METHODS: This study included a consecutive series of patients who underwent 1- or 2-level lumbar spinal fusion surgery between June and November 2021. Particle counts were measured using a particle counter, specifically focusing on six different sizes of PM (0.3, 0.5, 1, 2.5, 5, and 10 µm). Additionally, measurements were taken for HCHO in parts per million (ppm). Monopolar cautery was used in the surgical setting. Systematic measurements were conducted at specific time points during the surgical procedures to assess the levels of PM and HCHO. Furthermore, the efficacy of surgical smoke suction was evaluated by comparing the PM levels with and without adjacent placement of suction. RESULTS: This study involved 35 patients, with measurements of both PM and HCHO taken in 27 cases. The remaining 8 cases had measurements only for PM. In this study, statistically significant quantitative changes in various PM sizes were observed when electrocautery was used during spine surgery (12.3 ± 1.7 vs 1975.7 ± 422.8, 3.4 ± 0.5 vs 250.1 ± 45.7, and 1.9 ± 0.2 vs 78.1 ± 13.3, respectively, for 2.5-, 5-, and 10-µm PM; p < 0.05). The level of HCHO was also significantly higher (0.085 ± 0.006 vs 0.131 ± 0.014 ppm, p < 0.05) with electrocautery use. Utilization of adjacent suction of surgical smoke during electrocautery demonstrated a statistically significant reduction in PM levels. CONCLUSIONS: The findings of this study highlight the potential surgical smoke-related hazards that spine surgeons may be exposed to in the operating room. Implementing simple interventions, such as utilizing nearby suction, can effectively minimize the amount of toxic surgical smoke and mitigate these risks.

Impact of a Rounding Checklist Implementation in the Trauma Intensive Care Unit on Clinical Outcomes.

We aimed to evaluate the effectiveness of an intensive care unit (ICU) round checklist, FAST HUGS BID (Feeding, Analgesia, Sedation, Thromboembolic prophylaxis, Head-of-bed elevation, Ulcer prophylaxis, Glycemic control, Spontaneous breathing trial, Bowel regimen, Indwelling catheter removal, and De-escalation of antibiotics-abbreviated as FD hereafter), in improving clinical outcomes in patients with severe trauma. We included patients admitted to our trauma ICU from 2016 to 2020 and divided them into two groups: before (before-FD, 2016-2017) and after (after-FD, 2019-2020) implementation of the checklist. We compared patient characteristics and clinical outcomes, including ICU and hospital length of stay (LOS) and in-hospital mortality. Survival analysis was performed using Kaplan-Meier curves and multivariable logistic regression models; furthermore, multiple linear regression analysis was used to identify independent factors associated with ICU and hospital LOS. Compared with the before-FD group, the after-FD group had significantly lower in-hospital mortality and complication rates, shorter ICU and hospital LOS, and reduced duration of mechanical ventilation. Moreover, implementation of the checklist was a significant independent factor in reducing ICU and hospital LOS and in-hospital mortality. Implementation of the FD checklist is associated with decreased ICU and hospital LOS and in-hospital mortality.

Successful treatment of a blunt retrohepatic vena cava injury using an atriocaval shunt without sternotomy or thoracotomy: a case report.

Retrohepatic vena cava (RHIVC) injury is often fatal and can be very difficult to manage. Total hepatic vascular isolation, a shunt, or bypass surgery is required for the surgical treatment of RHIVC injury in hemodynamically unstable patients; however, these are not easy procedures. Here, we present a case of RHIVC injury that was successfully treated by atriocaval shunt placement via a transdiaphragmatic incision without sternotomy or thoracotomy. In addition, we review the resuscitation and surgical procedures used for total hepatic vascular isolation in patients with RHIVC injury.

Colloidal Quantum Dot Nanolithography: Direct Patterning via Electron Beam Lithography.

Micro/nano patterns based on quantum dots (QDs) are of great interest for applications ranging from electronics to photonics to sensing devices for biomedical purposes. Several patterning methods have been developed, but all lack the precision and reproducibility required to fabricate precise, complex patterns of less than one micrometer in size, or require specialized crosslinking ligands, limiting their application. In this study, we present a novel approach to directly pattern QD nanopatterns by electron beam lithography using commercially available colloidal QDs without additional modifications. We have successfully generated reliable dot and line QD patterns with dimensions as small as 140 nm. In addition, we have shown that using a 10 nm SiO2 spacer layer on a 50 nm Au layer substrate can double the fluorescence intensity compared to QDs on the Au layer without SiO2. This method takes advantage of traditional nanolithography without the need for a resist layer.

Comparative Analysis of Biological Signatures between Freshly Preserved and Cryo-Preserved Bone Marrow Mesenchymal Stem Cells.

Mesenchymal stem cells (MSCs) can differentiate into multiple connective tissue lineages, including osteoblasts, chondrocytes, and adipocytes. MSCs secrete paracrine molecules that are associated with immunomodulation, anti-fibrotic effects, and angiogenesis. Due to their orchestrative potential, MSCs have been therapeutically applied for several diseases. An important aspect of this process is the delivery of high-quality MSCs to patients at the right time, and cryo-biology and cryo-preservation facilitate the advancement of the logistics thereof. This study aimed to compare the biological signatures between freshly preserved and cryo-preserved MSCs by using big data sourced from the Pharmicell database. From 2011 to 2022, data on approximately 2300 stem cell manufacturing cases were collected. The dataset included approximately 60 variables, including viability, population doubling time (PDT), immunophenotype, and soluble paracrine molecules. In the dataset, 671 cases with no missing data were able to receive approval from an Institutional Review Board and were analyzed. Among the 60 features included in the final dataset, 20 were selected by experts and abstracted into two features by using a principal component analysis. Circular clustering did not introduce any differences between the two MSC preservation methods. This pattern was also observed when using viability, cluster of differentiation (CD) markers, and paracrine molecular indices as inputs for unsupervised analysis. The individual average PDT and cell viability at most passages did not differ according to the preservation method. Most immunophenotypes (except for the CD14 marker) and paracrine molecules did not exhibit different mean levels or concentrations between the frozen and unfrozen MSC groups. Collectively, the biochemical signatures of the cryo-preserved and unfrozen bone marrow MSCs were comparable.

Field-portable seawater toxicity monitoring platform using lens-free shadow imaging technology.

The accidental spill of hazardous and noxious substances (HNSs) in the ocean has serious environmental and human health consequences. Assessing the ecotoxicity of seawater exposed to various HNS is challenging due to the constant development of new HNS or mixtures, and assessment methods are also limited. Microalgae viability tests are often used among the various biological indicators for ecotoxicity testing, as they are the primary producers in aquatic ecosystems. However, since the conventional cell growth rate test measures cell viability over three to four days using manual inspection under a conventional optical microscope, it is labor- and time-intensive and prone to subjective errors. In this study, we propose a rapid and automated method to evaluate seawater ecotoxicity by quantification of the morphological changes of microalgae exposed to more than 30 HNSs. This method was further validated using conventional growth rate test results. Dunaliella tertiolecta, a microalgae species without rigid cell walls, was selected as the test organism. Its morphological changes in response to HNS exposure were measured at the single cell level using a custom-developed device that uses lens-free shadow imaging technology. The ecotoxicity evaluation induced by the morphological change could be available in as little as 5 min using the proposed method and device, and it could be effective for 20 HNSs out of 30 HNSs tested. Moreover, the test results of six selected HNSs with high marine transport volume and toxicity revealed that the sensitivity of the proposed method extends to half the maximum effective concentration (EC50) and even to the lowest observed effective concentration (LOEC). Furthermore, the average correlation index between the growth inhibition test (three to four days) and the proposed morphology changes test (5 min) for the six selected HNSs was 0.84, indicating great promise in the field of various point-of-care water quality monitoring. Thus, the proposed equipment and technology may provide a viable alternative to traditional on-site toxicity testing, and the potential of rapid morphological analysis may replace traditional growth inhibition testing.

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland-Altman analysis confirmed the model's reliability, with a mean bias of -2.29 and 95% limits of agreement between 18.49 and -23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the "order ratio" and "minimum ratio" were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

CASS: A distributed network clustering algorithm based on structure similarity for large-scale network.

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions.

A review of recent progress in lens-free imaging and sensing.

Recently, lens-free imaging has evolved as an alternative imaging technology. The key advantages of this technology, including simplicity, compactness, low cost, and flexibility of integration with other components, have facilitated the realization of many innovative applications, especially, in the fields of the on-chip lens-free imaging and sensing. In this review, we discuss the development of lens-free imaging, from theory to applications. This article includes the working principle of lens-free digital inline holography (DIH) with coherent and semi coherent light, on-chip lens-free fluorescence imaging and sensing, lens-free on-chip tomography, lens-free on-chip gigapixel nanoscopy, detection of nanoparticles using on-chip microscopy, wide field microscopy, and lens-free shadow image based point-of-care systems. Additionally, this review also discusses the lens-free fluorescent imaging and its dependence on structure and optical design, the advantage of using the compact lens-free driven equilibrium Fourier transform (DEFT) resolved imaging technique for on-chip tomography, the pixel super-resolved algorithm for gigapixel imaging, and the lens-free technology for point-of-care applications. All these low-cost, compact, and fast-processing lens-free imaging and sensing techniques may play a crucial role especially in the fields of environmental, pharmaceutical, biological, and clinical applications of the resource-limited settings.