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Sara Moradi Tuchayi,1 Ying Wang,1 Isaac J Pence,1 Alex Fast,1 Anat Stemmer-Rachamimov,2 Conor L Evans,1 R Rox Anderson,1 Lilit Garibyan1 1Wellman Center for Photomedicine, Massachusetts General Hospital, Department of Dermatology, Harvard Medical School, Boston, MA, USA; 2Massachusetts General Hospital and Department of Pathology, Harvard Medical School, Boston, MA, USACorrespondence: Lilit Garibyan, Wellman Center for Photomedicine, Massachusetts General Hospital, 50 Blossom Street-Thier 2, Boston, MA, 02114, USA, Tel +1 617-726-2005, Email lgaribyan@mgh.harvard.eduBackground: Cryoneurolysis uses tissue cooling as an opioid-sparing, long-lasting treatment for peripheral nerve pain. A nerve-selective method for cryoneurolysis by local injection of ice-slurry was developed to allow cryoneurolysis to be performed with a standard needle and syringe, similar to peripheral nerve blocks. Since the treatment of patients with chronic pain may require repeated injections, we investigated the safety and tolerance of repeated treatments in a rat model.Methods: Three repeated ice-slurry treatments, given 6 weeks apart were performed around the rat sciatic nerve. Nerve and surrounding tissues were collected up to 4 months after the third treatment for analysis. Coherent anti-Stokes Raman scattering (CARS) microscopy was used to study effects on myelin sheaths and axon structure. Immunofluorescence (IF) staining was used to study effects on axon density. Hematoxylin and Eosin (H&E) staining was used to examine histologic effects on sciatic nerve and surrounding tissue.Results: Histologic and CARS image analysis of nerve tissue collected months after three injections demonstrated recovery of nerve structure, myelin organization and axon density to baseline levels, without any residual inflammation, scarring or neuroma formation. No inflammation or scarring was detected in surrounding skin and muscle tissues.Conclusion: Repeated ice-slurry injections cause temporary, nerve-selective and reversible changes in the peripheral nerve. There was no histologic damage to surrounding skin and muscle tissues. Repeated treatments with injectable ice-slurry for cryoneurolysis appear to be safe and well tolerated. Clinical studies for patients with chronic pain are warranted.Keywords: pain treatment, slurry, cooling, multiple treatments

Animal waste can be converted into a renewable energy source using biogas technology. This process has an impact on greenhouse gas emissions and is a sustainable source of energy for countries. It can reduce the effects of climate change and protect the planet for future generations. Tier1 and tier2 approaches are commonly used in the literature to calculate emissions factors. With boosting algorithms, this study estimated each animal category's biogas potential and CH 4 emissions (tier1 and tier2 approach) for 2004-2021 in all of Turkey's provinces. Two different scenarios were created in the study. For scenario-1, the years 2020-2021 were predicted using data from 2004 to 2019, while for scenario-2, the years 2022-2024 were predicted using data from 2004 to 2021. According to the scenario-1 analysis, the eXtreme Gradient Boosting Regressor (XGBR) algorithm was the most successful algorithm with an R 2 of 0.9883 for animal-based biogas prediction and 0.9835 and 0.9773 for animal-based CH 4 emission predictions (tier1 and tier2 approaches) for the years 2020-2021. When the mean absolute percentage error was evaluated, it was found to be relatively low at 0.46%, 1.07%, and 2.78%, respectively. According to the scenario-2 analysis, the XGBR algorithm predicted the log10 values of the animal-based biogas potential of five major cities in Turkey for the year 2024, with 11.279 for Istanbul, 12.055 for Ankara, 12.309 for Izmir, 11.869 for Bursa, and 12.866 for Antalya. In the estimation of log10 values of CH 4 emission, the tier1 approach yielded estimates of 3.080, 3.652, 3.929, 3.411, and 3.321, respectively, while the tier2 approach yielded estimates of 1.810, 2.806, 2.757, 2.552 and 2.122, respectively., (© 2024. The Author(s).)

In Turkey, facilities for the use of biomass resources in energy production are increasing, and new conversion facilities are commissioned every year to provide environmentally friendly energy production. Therefore, reliable energy potential estimates are needed. In this study, the animal manure-based-biogas potentials of Antalya, Isparta, and Burdur provinces in the Western Mediterranean Region of Turkey were calculated. Here, special information on cattle, small ruminants, and poultry, and animal age, number, and manure amount information were used in detail. In addition, carbon dioxide emissions, coal, electricity, and thermal energy, methane emission values with the Tier 1 and Tier 2 approaches were calculated and predicted by machine learning algorithms. To determine the model with the best results, machine learning algorithms support vector machine (SVM), multi-layer perceptron (MLP), and linear regression (LR) were used, and hyper-parameter optimization was performed. According to the results of biogas potential, CO 2 emission, electricity production, and thermal energy estimations SVM models are seen as the best models with R 2  = 0.999. When the coal amount estimation is examined, the LR models produce better results than SVM and MLP with R 2  = 0.997. In the estimation of CH 4 using the Tier 1 approach, the MLP model can perform the best estimation with R 2  = 0.977. In the CH 4 modeling obtained using the Tier 2 approach, the LR models were superior to the other models with the performance value of R 2  = 0.962., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis., Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms., Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis., Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results., Competing Interests: No potential conflict of interest relevant to this article was reported., (Copyright © 2023. Korean Academy of Periodontology.)

In this two-part review we present an up-to-date description of different imaging methods available to map the localization of drugs on skin as a complement of established ex-vivo absorption studies. This first part deals with invasive methods which are grouped in two classes according to their underlying principles: i) methods using radioactivity such as autoradiography and ii) mass spectrometry methods such as MALDI and SIMS. For each method, a description of the principle is given along with example applications of imaging and quantifying drug delivery in human skin. Thanks to these techniques a better assessment of the fate of drugs is obtained: its localization on a particular skin structure, its potential accumulation, etc. A critical comparison in terms of capabilities, sensitivity and practical applicability is included that will help the reader to select the most appropriate technique depending on the particular problem to be solved., (Copyright © 2019 Elsevier B.V. All rights reserved.)

We present a rapidly tunable dual-output all-fiber light source for coherent Raman imaging, based on a dispersively matched mode-locked laser pumping a parametric oscillator. Output pump and Stokes pulses with a maximal power of 170 and 400 mW, respectively, and equal durations of 7 ps could be generated. The tuning mechanism required no mechanical delay line, enabling all-electronic arbitrary wavelength switching across more than 2700  cm - 1 in less than 5 ms. The compact setup showed a reliable operation despite mechanical shocks of more than 25  m / s 2 and is, thus, well suited for operation in a mobile cart. Imaging mouse and human skin tissue with both the portable light source and a commercial laboratory-bound reference system yielded qualitatively equal results and verified the portable light source being well suited for coherent Raman microscopy., Competing Interests: M. Brinkmann and T. Hellwig are pursuing the commercialization of the presented light source by means of the company Refined Laser Systems UG (haftungsbeschränkt), Münster, Germany (I,P,S)., (Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Clinical diagnostic devices provide new sources of information that give insight about the state of health which can then be used to manage patient care. These tools can be as simple as an otoscope to better visualize the ear canal or as complex as a wireless capsule endoscope to monitor the gastrointestinal tract. It is with tools such as these that medical practitioners can determine when a patient is healthy and to make an appropriate diagnosis when he/she is not. The goal of diagnostic medicine then is to efficiently determine the presence and cause of disease in order to provide the most appropriate intervention. The earliest form of medical diagnostics relied on the eye - direct visual observation of the interaction of light with the sample. This technique was espoused by Hippocrates in his 5th century BCE work Epidemics, in which the pallor of a patient's skin and the coloring of the bodily fluids could be indicative of health. In the last hundred years, medical diagnosis has moved from relying on visual inspection to relying on numerous technological tools that are based on various types of interaction of the sample with different types of energy - light, ultrasound, radio waves, X-rays etc. Modern advances in science and technology have depended on enhancing technologies for the detection of these interactions for improved visualization of human health. Optical methods have been focused on providing this information in the micron to millimeter scale while ultrasound, X-ray, and radio waves have been key in aiding in the millimeter to centimeter scale. While a few optical technologies have achieved the status of medical instruments, many remain in the research and development phase despite persistent efforts by many researchers in the translation of these methods for clinical care. Of these, Raman spectroscopy has been described as a sensitive method that can provide biochemical information about tissue state while maintaining the capability of delivering this information in real-time, non-invasively, and in an automated manner. This review presents the various instrumentation considerations relevant to the clinical implementation of Raman spectroscopy and reviews a subset of interesting applications that have successfully demonstrated the efficacy of this technique for clinical diagnostics and monitoring in large (n ≥ 50) in vivo human studies.

Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated via surgical resection. Inadequate resection can lead to local recurrence and decreased survival rates. In this study, we investigate the hypothesis that near-infrared (NIR) autofluorescence can be utilized for tumor margin analysis by differentiating STS from the surrounding normal tissue. Intraoperative in vivo measurements were acquired from 30 patients undergoing STS resection and were characterized to differentiate between normal tissue and STS. Overall, normal muscle and fat were observed to have the highest and lowest autofluorescence intensities, respectively, with STS falling in between. With the exclusion of well-differentiated liposarcomas, the algorithm's accuracy for classifying muscle, fat, and STS was 93%, 92%, and 88%, respectively. These findings suggest that NIR autofluorescence spectroscopy has potential as a rapid and nondestructive surgical guidance tool that can inform surgeons of suspicious margins in need of immediate re-excision.