A novel core–shell structured composite of UiO-66-Tu/PAN nanofiber membranes (NFMs) functionalized with thiourea (Tu) was developed for the efficient separation of Co(II) from simulated radioactive wastewater. Various analytical techniques were employed to characterize the structure and properties of the NFMs. The filtration performance, flux, and hydrophilicity of the membrane were evaluated using a membrane flux tester and contact angle (CA) measurements, respectively. The adsorption performance of UiO-66-Tu/PAN for Co(II) was investigated through batch adsorption experiments. The results indicate that at a UiO-66-Tu/PAN content of 7 %, the UiO-66-Tu/PAN NFMs exhibit better hydrophilicity (CA of 37.8°) and a higher cobalt ion retention rate (77.41 %). Under optimal conditions, the theoretical maximum adsorption capacity for cobalt ions is 51.2 mg/g. X-ray photoelectron spectroscopy (XPS) analysis was utilized to clarify the adsorption mechanism of Co(II) by the 7 %UiO-66-Tu/PAN composite, showing a coordination mechanism that involves the participation of oxygen, nitrogen, and sulfur atoms in the ligand. In addition, the material also demonstrates excellent radiation stability (50–100 kGy). It should be pointed out that this is the first time that metal–organic frameworks (MOFs) core–shell nanofibers membranes have been used for cobalt ion separation. The development of this new material effectively addresses the challenges associated with using MOFs as powdered adsorbents, achieving efficient purification of wastewater and providing important engineering significance for the treatment and disposal of trace amounts of 60Co radioactive wastewater. 

Facile construction of a core-shell structured metal-organic frameworks nanofiber membrane for removing Co(II) from simulated radioactive wastewater

Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem. 

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Treated Kaolin Clay Incorporated with Nickel Nanoparticles for Enhanced Removal of Crystal Violet and Methyl Orange from Textile Wastewater

This study addresses the need for high-performance and sustainable air filters by developing a bio-based, high-efficiency particulate air (HEPA) filter. Current HEPA filters often rely on non-biodegradable materials, creating environmental burdens. In this paper, we presented a HEPA filter fabricated from natural basalt fiber (BF) and nanocellulose fiber. The developed filter featured a sandwich structure with electrospun nanocellulose fiber deposited onto a base BF layer, followed by a second BF layer and heat treatment. Various techniques were employed to characterize the obtained sample, and the results showed that the nonwoven BF fabric significantly reduced the pressure drop of the filter by up to 60 %. The nanocellulose fiber played a crucial role in achieving a remarkable filtration efficiency of 99.99 % for PM 

Eco-friendly and highly efficient PM0.3 air filter made from nonwoven basalt fiber and electrospun nanocellulose fiber

Electret fiber-based air filtration materials find extensive applications in various fields, including particulate matter filtration, indoor purification, and epidemic prevention. However, achieving high-efficiency filtration with low resistance and long-term stability remains a challenge. In this study, we present a novel approach to fabricate sheath-core bicomponent spunbond filters (BCSFs) doped with polytetrafluoroethylene nanoparticles (PNPs) using bicomponent spunbond, through-air bonding, and corona charging techniques. This strategy enables the filtration material to possess low air resistance, long-term stable filtration efficiency, and a high dust holding capacity. The resulting BCSFs-PNPs exhibit remarkable filtration performance, with a filtration efficiency of 99.25% and a dust holding capacity of up to 11.48 g m−2, while maintaining a pressure drop as low as 37.87 Pa. Notably, the filtration efficiency of BCSFs-PNPs only experienced a slight decrease of 3.58% over a period of 180 days, demonstrating excellent long-term stability. The successful preparation of BCSFs-PNPs using corona charging technology holds great promise and offers valuable insight for the design and application of advanced air filtration materials. 

Bicomponent spunbond filters doped with polytetrafluoroethylene nanoparticles for long-term efficient fine particle removal

Multi-Scale Nanofiber Membrane Functionalized with Metal-Organic Frameworks for Efficient Filtration of Both PM2.5 and CH3CHO with Colorimetric NH3 Detection

This study demonstrates a process to produce a diverse functional membrane that comprises nylon 6,6 and metal–organic framework (MOF) particles to purify environments of toxic gases, heavy metal ions, and waste dyes. Roll-to-roll hot pressing was used to fabricate a hybrid membrane of nylon 6,6 and adsorptive MOF [zeolitic imidazolate framework-8 (ZIF-8) and FeBTC]. In addition, two- and three-MOF layer membranes were prepared by repeating the hot pressing process, and the type and morphology of the synthesized MOFs were confirmed through scanning electron microscopy and X-ray diffraction. The results show that the larger the number of MOF layers, the greater the removal efficiency for toxic gases. The FeBTC@Nylon 3rd membrane removed 100% of the NH3 gas in 30 min, and the ZIF-8@Nylon 3rd membrane removed 100% of the H2S gas in 60 min. Both types of membrane have excellent adsorption performance for heavy metals and dye-contaminated wastewater, which otherwise harm the biodiversity of water bodies and destroy the food chain over extended periods and cause damage to humans. Kinetic model analysis revealed that the adsorption reaction for heavy metals followed the second-order kinetic model, and chemical adsorption was confirmed. These results highlight the significance of process design in determining an optimum membrane for the removal of a variety of toxic materials. 

Roll-to-Roll Processing of Hybrid MOF and Nylon Membranes for High Efficiency Removal of Toxic Gases, Heavy Metals, and Dyes

Facile and eco-friendly dyeing process based on UV-induced photografting to immobilize halochromic dye on nylon 6 fabric for acidic gas detection

Dear Editor, Early diagnosis and primary approach are important for achieving a positive prognosis in melanoma.1 Although excisional biopsy is the standard diagnostic method, incisional or punch biopsy is often used for large lesions or those with limited skin laxity, still having drawbacks of falsenegative results resulting from an inappropriate biopsy site.2– 4 To aid in primary medical access, we applied a noninvasive, augmented approach to punch biopsies in diagnosing melanoma by suggesting biopsy sites based on dermoscopy images created by Generative adversarial network (GAN).5 From 2009 to 2019, 708 dermoscopy images were retrospectively collected from patients who visited Seoul St. Mary's Hospital. The associated biopsy results were confirmed and were merged with 9880 images from the open HAM10000 melanoma data set.6 After excluding inappropriate images (scalp lesions, lowquality), 7126 nevi and 1259 invasive melanomas were included and fitted as 224 × 224 and 1024 × 1024 pixels for training classifier and generator, respectively. The classifier was trained to determine whether the input image was benign or malignant, and the generator, trained to create images of benign nevi based on styleGAN2, creates an image that resemble the input image, but without features of melanoma using styleGAN2 (Figure 1a). Input images were compared to generatorcreated images to determine potential biopsy sites based on pixelwise distance map, in which the distance of pixels at same position in two images is defined as 3dimensional (RGB value for each axis) space. The model determines few farthest points and depicts them as red points along with priority. Three boardcertified dermatologists determined the most appropriate area (confidence level 1) and the next most suitable area (confidence level 2) for biopsy; and then compared with the model's decision. The accuracy of the whole model was evaluated with four cutoffs, as described in detail in Table 1. Accuracy, sensitivity, specificity and F1score of the overall deep convolutional neural network classifier were 91.05%, 49.18%, 98.16% and 65.53%, respectively. The generator successfully trained the characteristics of nevus and when melanoma images were projected into the generator's latent space, images lacking the distinctive features of melanoma were created (Figure 1b). Regarding the accuracy of biopsy site recommendation compared with dermatologists, level A, B, C and D were reported as 58%, 90%, 78% and 98%, respectively (Table 1). In this study, we firstly applied an augmented approach to conventional punch biopsy in diagnosing melanoma by proposing a biopsy site. Most studies were based on a deeplearning algorithm focused on diagnosis and classification, and there are few on assisting diagnosis by suggesting the appropriate biopsy site.7,8 Dermoscopy increases the diagnostic accuracy when used by experienced specialists; however, it is time consuming, subjective and has the possibility of misdiagnosis when used by unskilled evaluators.9,10 Therefore, it is necessary to establish an automatic computeraided diagnosis system for melanoma and biopsy site recommendation to aid decisionmaking not only by dermatologists but also by primary care physicians not skilled in using dermoscopy. Although the classifier was designed with high specificity to rule out benign lesions, and the biopsy site recommendation accuracy (levels AD) remains insufficient, collection of additional highquality data sets will increase the accuracy of the model to be applied to a real medical environment. Despite the limitation that validation was not performed by comparing the histopathologic results, we tried to overcome differences in melanoma subtypes between races using both the Korean and Caucasian data sets. Moreover, using a large number of dermoscopy images, we firstly devised a model that can assist in determining the biopsy site. This pilot study showed its possible applications in a realworld setting. Through further research, we expect our method to bring better prognosis, greater convenience and reduced medical costs.

Artificial intelligence‐based biopsy site recommendation solution for melanoma: A pilot study

Colorimetric Multi-Scale Nanofiber Membrane Functionalized with Metal-Organic Frameworks for the Simultaneous Removal of Pm2.5 and Toxic Gases

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