algorithm

Ulcers are one of the most common lesions in the oral cavity and are caused by injuries to the oral mucosa for different reasons. These ulcers are clinically characterized by the loss of the whole thickness of the epithelium and the denuding of the underlying lamina propria in the presence of a white/yellow layer called the fibrinoleukocytic layer and are usually accompanied by pain. Due to the wide array of etiologic agents for oral ulcers, pinpointing the etiologic agent can be highly challenging, and the availability of a good diagnostic guide assists in the diagnosis and selection of proper treatment modalities because a fast and accurate diagnosis of some ulcers (e.g., the early stages of squamous cell carcinoma) is crucial for the early initiation of treatment. The present study attempts to present a proper and comprehensive diagnostic guide to facilitate the classification and diagnosis of different oral ulcers.

We are writing to express my appreciation for the insightful article on pain management in trauma patients and its critical role in improving patient outcomes. The current protocol utilizing acetaminophen and opiate agents has proven to be significantly effective. However, Recent studies have indicated that the combination of acetaminophen and NSAIDs can provide superior analgesic effects compared to either agent alone, potentially surpassing the effect of opioids The current protocol utilizing acetaminophen and opiate agents has proven to be significantly effective. However, Recent studies have indicated that the combination of acetaminophen and NSAIDs can provide superior analgesic effects compared to either agent alone, potentially surpassing the effect of opioids. Integrating NSAIDs into the pain management protocol could offer several benefits, including enhanced Pain Relief (3) and Reduced Opioid Consumption. we believe that incorporating acetaminophen plus NSAIDs into the existing pain management algorithm could be a valuable step towards optimizing care for trauma patients. Enclosed is a revised proposed algorithm based on your previously published protocol for the management of acute pain in trauma patients.

Congenital Chylothorax (CC) is a rare condition, which is defined as an accumulation of the chyle in the pleural cavity;moreover, it is associated with significant morbidities, including respiratory distress, malnutrition, immunodeficiency, and infections.

 Case Report:

 The diagnosis of chylothorax was made upon count cell analysis of the pleural fluid with ≥80% lymphocytesdetected before birth or within 28 days after birth. In this study, we presented five cases of CC infants. They were dischargedfrom our tertiary center at Hedi Chaker Hospital, Sfax, Tunisia, from January 2010 to December 2018. There were three malesand two females. Prenatal diagnosis was made in four cases. There were four full-term newborns and one near-term of 36weeks. Pleural effusion was on the right side in three cases, on the left side in one case, and bilateral in one case. Four casesrequired mechanical ventilation. Somatostatin was indicated in one case. The treatment was successful in four cases. One casepresented a dysmorphic syndrome and died of pneumothorax.

The treatment of CC is based on conservative management. Somatostatin or its analog octreotide is considered anadjunctive treatment of CC. However, the refractory cases are treated with chemical pleurodesis or surgical treatment. Wepropose an algorithm for the treatment of CC.

Anemia stands out as the most prevalent blood disorder globally. Various factors can contribute to its development, including insufficient production of red blood cells (RBCs) as well as degradation of RBCs. The functional impairment of RBCs in anemia can result in a wide spectrum of symptoms, ranging from fatigue and weakness to severe, life-threatening conditions. The primary method for detecting anemia is the commonly employed complete blood count (CBC) test. However, for certain cases and to distinguish between different types of anemia, more advanced tests become necessary. Artificial intelligence (AI) has emerged as a technology designed to replicate human intelligence and perform tasks that typically require human cognitive abilities. AI models possess the capability to comprehend patterns and associations, enabling them to recognize and analyze images. In the context of anemia, studies have demonstrated that AI algorithms can analyze images of various physical characteristics such as conjunctiva, palm, tongue, and fingernails. By estimating hemoglobin concentration, these algorithms can predict the presence of anemia. Furthermore, AI systems have also exhibited the ability to analyze clinical data, including laboratory tests and blood smears, to predict anemia and identify specific types. It is worth noting that previous studies in the context if AI applications in anemia have been conducted on relatively small populations. However, the accuracy achieved in these investigations has been satisfactory, suggesting that AI systems could potentially play a significant role in the future of anemia diagnosis and management.

Several aquaculture industries in underdeveloped nations use fossil fuel-powered generators to produce electricity. This pattern has raised greenhouse gas emissions as well as the price of aquaculture products.

To address this issue, this study contains a bi-objective model that optimizes the parametric settings of waste-to-energy (WTE) plants for aquaculture firms: Levelized cost of energy and power expenses for reverse logistics. The best values for these objectives were created using a genetic algorithm and goal programming.

Four planning periods were taken into account during implementation, and actual data were gathered from a Nigerian aquaculture company. The electricity costs from biodiesel ranged from N0.7541 per kW to N0.7628 per kW, respectively. Reverse logistics has energy costs ranging from N6 329 492.10 to N7 121 015.53. The proposed model produced average values for several WTE parametric parameters, including a 1.69 million kg hydrogen gas, a 59.16% hydrogen gas compression efficiency, and an 83.39% electricity conversion efficiency. Furthermore, the system had logistics’ minimum and maximum fractions of 0.18% and 21%, respectively.

Our findings demonstrated how WTE parametric parameters impact the aquaculture industry’s electrical power unit.

Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend.  This study aimed to diagnosis the sleep apnea types using the optimized neural network. This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used.  The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and %96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy.   Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

Reports of acute hepatitis with unknown origin in children have been published worldwide since April 2022. Due to the unfamiliar nature of the disease and the alarming trend of increasing reports worldwide, health workers must become aware of its diagnosis and treatment. Accordingly, the committee for developing guidelines for the Department of Pediatrics, Tehran University of Medical Sciences, prepared a clinical guideline for more efficient management of these patients. Due to the limited information about this disease, it has been tried to categorize and specify the required diagnostic and treatment measures based on the latest published data. Herein we express this algorithmic approach and diagnostic and therapeutic guidelines for acute hepatitis with unknown origin in children.

Multiple Sclerosis (MS) is a common chronic disease that affects society, especially young people. In recent years, data sciences have been used extensively to deal with the disease. Machine learning is one of the main data sciences types which has been used to deal with chronic diseases such as MS. This study aimed to identify the applications of machine learning algorithms in MS disease. This study is a systematic review that conducted in 2020. The searches were done in PubMed, Scopus, ISI Web of Sciences, Ovid, Science Direct, Embase, and Proquest scientific databases, by combining related keywords. Data extraction was done by using a data extraction form to follow the trends of this field of study. The results of the study showed that diagnosis of MS was the main application of machine learning in MS (33.3 %); also, assessment (24.24%) and prediction (18.18 %) of the disease were other main applications. The most used data type was medical images such as MRI and CT scans (55.17 %). The most used machine learning algorithm type was Support Vector Machine (SVM) (30 %) as a classification algorithm. The most optimized algorithm for the diagnosis and prediction of MS was KNN. It’s suggested to use machine learning algorithms to diagnose, assess, predict lesion classification, treatment, and severity determining of MS disease. Although the most common form of data used for MS is medical images, it is suggested that other types of data are generated to be used in machine learning algorithms. Considering the optimization rate of the algorithms used, it is suggested to pay more attention to the type of data and study objectives in data analysis using machine learning.

Patient-specific quality assurance (PSQA) assumes a vital role in precise and accurate radiation delivery to cancer patients. Since the patient body comprises heterogeneous media, the present study aimed to fabricate a heterogeneous thoracic phantom for PSQA.

Heterogeneous thoracic (HT) phantom was fabricated using rib cage madeup of bone equivalent material, kailwood to mimic lungs and wax to mimic various body parts. Physical density of all these materials used in phantom fabrication was measured and compared with that of the corresponding part of actual human thorax. One beam was planned on the computed tomography (CT) images of phantom and actual patient thorax region. Dose distribution in both the plans was measured and analyzed.

The estimated densities of heart, lung, ribs, scapula, spine, and chest wall tissues were 0.804±0.007, 0.186±0.010, 1.796±0.061, 2.017±0.026, 2.106±0.029 and 0.739±0.028 respectively in case of HT phantom while 1.038±0.010, 0.199±0.031, 1.715±0.040, 2.006±0.019, 1.929±0.065 and 0.816±0.028 g/cc, respectively in case of actual human thorax region.The depths of isodose curves in HT phantom were also comparable to the isodose curve’s depths inreal patient. PSQA results were within ±3% for flat beam (FB) and flattening filtered free beam (FFFB) of 6 megavolts (MV) energy.

Density and dose distribution pattern in HT phantom were similar to that in actual human thorax region. Thus, fabricated HT phantom can be utilized for radiation dosimetry in thoracic cancer patients. The materials used to develop HT phantom are easily available in market at an affordable price and easy to craft.

It is necessary to evaluate fertility effective agents to predict assisted reproduction outcomes. This study was designed to examine sperm vacuole characteristics, and its association with sperm chromatin status and protamine-1 (PRM1) to protamine-2 (PRM2) ratio, to predict assisted pregnancy outcomes.

In this experimental study, ninety eight semen samples from infertile men were classified based on Vanderzwalmen’s criteria as follows: grade I: no vacuoles; grade II: ≤2 small vacuoles; grade III: ≥1 large vacuole and grade IV: large vacuole with other abnormalities. The location, frequency and size of vacuoles were assessed using high magnification, a deep learning algorithm, and scanning electron microscopy (SEM). The chromatin integrity, condensation, viability and acrosome integrity, and protamination status were evaluated for vacuolated samples by toluidine blue (TB) staining, aniline blue, triple staining, and CMA3 staining, respectively. Also, Protamine-1 and protamine-2 genes expression was analysed by reverse transcription-quantitative polymerase chain reaction (PCR). The assisted reproduction outcomes were also followed for each cycle.

The results show a significant correlation between the vacuole size (III and IV) and abnormal sperm chromatin condensation (P=0.03 and P=0.02, respectively), and also, protamine-deficient (P=0.04 and P=0.03, respectively). The percentage of reacting acrosomes was significantly higher in the grades III and IV spermatozoa in comparison with normal group. The vacuolated spermatozoa with grade IV showed a high protamine mRNA ratio (PRM-2 was underexpressed, P=0.01). In the IVF cycles, we observed a negative association between sperm head vacuole and fertilization rate (P=0.01). This negative association was also significantly observed in pregnancy and live birth rate in the groups with grade III and IV (P=0.04 and P=0.03, respectively).

The results of our study highlight the importance sperm parameters such as sperm head vacuole characteristics, particularly those parameters with the potency of reflecting protamine-deficiency and in vitro fertilization/ intracytoplasmic sperm injection (IVF/ICSI) outcomes predicting.

Context:

 First cases of Coronavirus disease 2019 (COVID-19) were reported in December 2019. With more than 100 million confirmed cases 14 months later, the disease has become the worst public-health dilemma of the century. The rapid global spread of COVID-19 has resulted in an international health emergency, threatening to overwhelm health care systems in many parts of the world, especially poor resource countries.

Evidence Acquisition: 

Influenza and COVID-19 have similar clinical symptoms, and both cause a respiratory illness that may vary from mild to severe. Both diseases have the same mode of transmission and require similar public health guidelines to prevent their spread, but their treatment strategies are different. In this study, an algorithmic method is proposed for managing patients according to their symptoms for each of these infections.

 In fall and winter, infections with seasonal influenza and other respiratory viruses become common. As influenza also causes significant morbidity and mortality, especially at the two extremes of age and in those with compromised immunity, it is of major importance to know the similarities and dissimilarities between COVID-19 and seasonal influenza and plan appropriate public health measures to deal with each of these illnesses.

 Will there be a devastating combined epidemic of COVID-19 and influenza (COV-Flu) during the 2020 - 2021 season? Does co-infection increase the risk of severe illness or amplify virus shedding? Actually, we do not yet know the answers to these questions; so, in this article, first, we attempt to define the similarities and differences between COV-Flu. Then, we will have a brief discussion on how to manage patients presenting with symptoms suggestive of both diseases. However, as COVID-19 has been recognized as a pandemic since December 2019, the management of this emerging disease is rapidly evolving as new information is collected from different parts of the world.

TrueBeam STx® latest generation linear accelerators (linacs) were installed at Sheikh Khalifa International University Hospital Casablanca, Morocco, this study aimed to present and analyse the dosimetric characteristics obtained during the commissioning.

Dosimetric parameters, including percentage depth dose, profiles, output factor, multileaf collimator (MLC) transmission, and dosimetric leaf gaps (DLG) factors were systematically measured for commissioning. Moreover, six photons beams (i.e., X6MV, X6FFFMV, X10MV, X10FFFMV, X15MV, and X18MV) were examined in this study, and a comparison was made between flattening filter (FF) and flattening filter free (FFF) beams.

According to the results, the FF and FFF beams symmetry and flatness were in the tolerance intervals. The unflattness values were estimated at 1.1% and 1.2% for X6FFFMV and X10FFFMV, respectively. Furthermore, tissue phantom ratio(20/10)(TPR) values of the FF beams were X6MV, 0.664; X10MV, 0.738; X15MV, 0.761; and X18MV, 0.778, and the TPR (20/10) values of the FFF beams included 0.632 and 0.703 for 6FFFMV and 10FFFMV, respectively. The results also revealed that the output factor values increased with field size, the surface dose decreased with increasing energy, and the FFF obtained lower mean energy. The MLC transmissions factors were 0.0121, 0.0103, 0.0136, 0.0122, 0.0133,  and 0.0121 for X6, X6FFF, X10, X10FFF, X15, and X18, respectively; additionally, the DLG factors were obtained at 0.32, 0.26, 0.41, 0.37, 0.42, and 0.38 mm for X6, X6FFF, X10, X10FFF, X15, and X18, respectively.

Photon beams reference dosimetric characteristics were successfully matched with the international recommendations and vendor technical specifications.

In vitro dosimetric verification prior to patient treatment plays a key role in accurate and precision radiotherapy treatment delivery. Since the human body is a heterogeneous medium, the aim of this study was to design a heterogeneous pelvic phantom for radiotherapy quality assurance.
A pelvic phantom was designed using wax, pelvic bone, borax powder, and water mimicking different biological tissues. Hounsfield units and relative electron densities were measured. Various intensity-modulated radiotherapy (IMRT) plans were imported to the pelvic phantom for verification and implemented on the Delta 4 phantom. The quantitative evaluation was performed in terms of dose deviation, distance to agreement, and gamma index passing rate.
According to the results of the CT images of an actual patient, relative electron densities for bone, fat, air cavity, bladder, and rectum were 1.335, 0.955, 0.158, 1.039, and 1.054, respectively. Moreover, the CT images of a heterogeneous pelvic phantom showed the relative electron densities for bone, fat (wax), air cavity, bladder (water), and rectum (borax powder) as 1.632, 0.896, 0.159, 1.037, and 1.051, respectively.The mean percentage variation between planned and measured doses was found to be 2.13% within the tolerance limit (< ±3%) .In all test cases, the gamma index passing rate was greater than 90%.
The findings showed the suitability of the materials used in the design of the heterogeneous phantom. Therefore, it can be concluded that the designed phantom can be used for regular radiotherapy quality assurance

The aim of the present study was to evaluate how left ventricular twist and torsion are associated with sex between sex groups of the same age.

In this analytical study, twenty one healthy subjects were scanned in left ventricle basal and apical short axis views to run the block matching algorithm; instantaneous changes in the base and apex rotation angels were estimated by this algorithm and then instantaneous changes of the twist and torsion were calculated over the cardiac cycle.

The rotation amount between the consecutive frames in basal and apical levels was extracted from short axis views by tracking the speckle pattern of images. The maximum basal rotation angle for men and women were -6.94°±1.84 and 9.85°±2.36 degrees (p-value = 0.054), respectively. Apex maximum rotation for men was -8.89°±2.04 and for women was 12.18°±2.33 (p-value < 0.05). The peak of twist angle for men and women was 16.78 ± 1.83 and 20.95± 2.09 degrees (p-value < 0.05), respectively. In men and women groups, the peak of calculated torsion angle was 5.49°±1.04 and 7.12± 1.38 degrees (p-value < 0.05), respectively.

The conclusion is that although torsion is an efficient parameter for left ventricle function assessment, because it can take in account the heart diameter and length, statistic evaluation of the results shows that among men and women LV mechanical parameters are significantly different. This study was mainly ascribed to the dependency of the torsion and twist on patient sex.

The accuracy of dose calculation algorithm (DCA) is highly considered in the radiotherapy sequences. This study aims at assessing the accuracy of five dose calculation algorithms in tissue inhomogeneity corrections, based on the International Atomic Energy Agency TEC-DOC 1583.
A heterogeneous phantom was scanned using computed tomography and tests were planned on three-dimensional treatment planning systems (3D TPSs) based on IAEA TEC-DOC 1583.Doseswere measured for 6- and 18-MV photon beams with ion chambers and then the deviation between measured and calculated TPS doses were reported. The evaluated five DCAs include Monte Carlo (MC) algorithm employed by Monaco, pencil beam convolution (PBC) and anisotropic analytical algorithms (AAA) employed by Eclipse and Superposition (SP), and Clarkson algorithms employed by PCRT3D TPSs.
In Clarkson algorithm, low and high energy photons indicated 7.1% and 14.8% deviations out of agreement criteria, respectively. The SP, AAA, and PBC algorithms indicated 0.9%, 7.4%, and 13.8% for low energy photon and 9.5%, 21.3%, and 23.2% for high energy photon deviations out of agreement criteria, respectively. However, MC algorithm showed 1.8% and less than 1% deviations at high and low energy photons, respectively.
The DCAs had different levels of accuracy in TPSs. Some simple DCAs, such as Clarkson, showed large deviations in some cases. Therefore, the transition to more advanced algorithms, such as MC would be desirable, particularly for the calculation in the presence of inhomogeneity or high energy beams.

In response to a serious incident involving an atrial fibrillation (AF) associated stroke, a quality improvement project was established to examine and improve all aspects of patient care for individuals presenting with acute AF to London’s North Middlesex University Hospital (NMUH).Materials and The presenting complaint was examined for 2,105 consecutive medical admissions to identify 100 patients (4.7%) with acute AF. For each patient, 36 indices and performance indicators were collected and analysed against international standards. Deficiencies were identified in documentation, risk stratification, anticoagulation and arrhythmia management decisions. With cross-specialty collaboration, a single-page AF management algorithm was established using sequential PDSA methodology, and a further 100 consecutive patients with acute AF were analysed prospectively. A composite end-point of adverse outcomes (AF-associated readmission, stroke, cardiac death or major bleeding) was examined. Algorithm implementation significantly reduced the proportion of patients exposed to unnecessary stroke risk (30% vs 4%, p<0.0001); improved identification and documentation of thromboembolic potential (50% vs 88%, p<0.0001), reduced incorrect drug decisions (12% vs 2%, p=0.01), reduced contraindicated rhythm control (8% vs 0%, p=0.007), and increased direct oral anticoagulant (DOAC) prescribing (38% vs 86%, p<0.0001) over warfarin. After a mean follow-up of 248 +/- 91 days, there was a significant reduction in composite adverse outcomes (22% vs 6%, p=0.0018). Using established quality improvement methodology and cost-neutral multi-disciplinary expertise, this novel management algorithm has significantly improved the quality and safety of care for patients with acute AF at NMUH.

Proteinuria is a common laboratory fnding among children and adolescents. It can be identifed as either a transient or a persistent fnding and can represent a benign condition or a serious disease. Pertinent medical literature for asymptomatic proteinuria in children and adolescents published in English was searched between January 1980 and May 2017 using PubMed, MEDLINE, EMBASE, and Google Scholar research databases. Of the 64 reviewed articles, 24 studies were eligible for inclusion. Random spot urine protein‑to‑creatinine (PCR) ratio is widely used to reliably detect proteinuria. The normal value for the spot PCR in children aged 2 years or older is less than 0.3. In children aged below 2 years, the PCR can be as high as 0.5. Orthostatic proteinuria is defned as urine PCR greater than 0.3 detected in a urine specimen during the daytime activity but less than 0.3 on the frst morning void specimen. PCR above 3.0 signifes heavy proteinuria as seen in nephrotic syndrome. Orthostatic proteinuria is a frequent cause of proteinuria in asymptomatic children and adolescents, which require no specifc therapy except for health maintenance follow‑up. Pediatric nephrologist referral is indicated when the proteinuria is constant and persists over 6 months or is associated with hematuria, hypertension, or renal dysfunction. We provide a simplifed diagnostic algorithm for evaluation of proteinuria in primary care adolescents who appear well and in whom proteinuria is incidentally discovered during a routine examination.