Polymer gel materials are formed by physically crosslinking and chemically crosslinking to form a gel network system with high mechanical properties and reversible performance. Due to their excellent mechanical properties and intelligence, polymer gel materials are widely used in biomedical, tissue engineering, artificial intelligence, firefighting and other fields. Given the current research status of polymer gels at home and abroad and the current application status of oilfield drilling, this paper reviews the mechanism of polymer gels formed by physically crosslinking and chemically crosslinking, summarizes the performance characteristics and the mechanism of action of polymer gels formed by non-covalent bonding, such as hydrophobic bonding, hydrogen bonding, electrostatic and Van der Waals interactions interactions, and covalent bonding such as imine bonding, acylhydrazone bonding and Diels-Alder reaction. The current status and outlook of the application of polymer gels in drilling fluids, fracturing fluids and enhanced oil recovery are also introduced. We expand the application fields of polymer gel materials and promote the development of polymer gel materials in a more intelligent direction. Polymer gel is a three-dimensional crosslinked network composed of molecules with hydrophilic (polar) and hydrophobic groups on the main chain and side chains of macromolecules, or with dissociated groups, and the solvent is fixed in the molecular network, which is a material form between liquid and solid. The research on polymer gels at home and abroad is relatively well-established; as early as the 1940s and 1950s, Flory and Stockmayer studied the phenomenon of dissolution of electrolyte gels, and in 1960, Otto Wichterle et al. discovered the first synthetic hydrogel ,3,4. PolPolymer gels are classified into physical crosslinking gels and chemical crosslinking gels. Physically crosslinking gels are formed by non-covalent bonding with each other rather than by crosslinking agents, mainly hydrophobic bonding, hydrogen bonding, electrostatic, van der Waals interactions forces and other interactions. Self-assembled, non-covalent interactions of polymer gels are weaker in strength, but they have dynamic reversibility and can achieve sol–gel mutual transformation. Chemically crosslinking gels are not easily degradable enough for environmental protection, although they have better structural stability and mechanical properties . CompareAlong with the development of oil exploration and development and the development of deep, ultra-deep, unconventional and low-grade hydrocarbon resources, the environment of development is becoming more and more severe. Polymer gel, due to its remarkable mechanical properties, has strong self-adaptability for sealing pores and fractures of different sizes and is widely applied in oil and gas drilling for leakage prevention, plugging, fracturing, cementing, dissection and water plugging of the formation and improving the enhanced oil recovery, etc. As such, polymeric gels are one of the most widely used chemicals in oil and gas wells . AccordiThis paper reviews the classification, principles and research progress of physical polymer gels based on hydrophobic bonding, hydrogen bonding, electrostatic and Van der Waals interactions, non-covalent bonding and polymer gels based on imine bonding, acylhydrazone bonding and dynamic chemical covalent bonding of a Diels–Alder reaction. The current situation and application prospects of polymer gel materials in oilfield drilling and production fields such as drilling fluids, fracturing fluids, enhanced oil recovery, etc. are summarized and sorted systematically to provide reference and inspiration for subsequent researchers.A polymer gel is a three-dimensional mesh structure formed by the polymerization of molecular chains through crosslinking, or a mesh structure with a solvent , similar to biological tissue. The crosslinked structure makes it insoluble in water, and it maintains a certain form, such as a hydrogel. Due to the stimulation of different factors, such as type of polymer gel, salt concentration, pH, temperature, electrical stimulation and light irradiation ,13,14,15The physically crosslinking gel is a type of non-covalent bonding, which has the characteristics of reversibility and high performance . PhysicaHydrophobic associative gels consist of crosslinking points of their three-dimensional networks by hydrophobic associative micro-regions, and this interaction between hydrophobic groups is dynamically reversible, so hydrophobic associative hydrogels have high mechanical strength, self-healing and secondary processing properties. The main methods for the synthesis of hydrophobic association gels are free radical copolymerization  and parent chemical modification . GeneralHydrogen bonding is a common type of non-covalent bonding, usually weaker than covalent bonding; furthermore, it has the advantages of adjustability, addition and orientation, which can be enhanced by the synergistic effect of different hydrogen bonds . HydrogeCurbituril (CB) is a macrocyclic compound similar to cyclodextrin (CD), and both have similar molecular properties . The amiHydrogels with only one hydrogen-bonding energy dissipation mechanism do not achieve satisfactory integrated mechanical properties, while multiple hydrogen bonds can achieve similar strength to covalent bonds or even stronger than covalent bonds. Li et al.  preparedElectrostatically interacting gel is a type of gel that is formed by the copolymerizing of oppositely charged monomers in high concentration. The random charge distribution of polymorphic chains results in the formation of multiple ionic bonds within and between chains with fluctuating strength distributions . The gelElectrostatically interacting gels are formed by reversible disruption and construction of strong and weak ionic bonds, and the prepared hydrogels also show responses to different stimuli . In addition, since multiple shape memory effects can be achieved by relatively independent physical interactions in the gel, synthetic shape memory gels can be performed by programmable combinations . Wang etVan der Waals interactions are an attractive force existing between molecules, which is much weaker than chemical bonding, but when a large number of van der Waals interactions are gathered, they also play a role in the self-assembly of gels as an auxiliary force. Van der Waals interactions are a kind of non-directional saturation that exists between molecules, and it has a decisive influence on the physicochemical properties of substances such as boiling point, melting point, the heat of vaporization, heat of melting, solubility, surface tension, viscosity, etc. Van der Waals interactions also play an important role in the formation of self-assembling structures . AmhararVan der Waals interactions are among the intermolecular interaction forces, which are generated between nonpolar molecules or polar molecules through induced dipole interaction and can be divided into three specific forces: induced force, dispersion force and orientation force. Since it is difficult to form colloids by self-assembly through van der Waals interactions between molecules alone, several forces often interact together during gel formation. Many researchers usually combine hydrogen bonding, electrostatic interactions, and hydrophobic association with Van der Waals interactions to form higher-strength gels . The theChemically crosslinking gels are a major class of polymer gels. It is formed by crosslinking in the form of covalent bonds between polymer chain segments and is non-spontaneous, where the polymer reacts with small molecule crosslinking agents  or the reaction is initiated by radiation  . StartinImine bonds are generated by the reversible condensation reaction of primary amines and reactive carbonyl groups , also known as the Schiff base, and the dynamic equilibrium of Schiff base bonds can be used to prepare self-healing hydrogels . DynamicAmong these reversible chemical bonds, the imine bond has been widely applied, especially in chitosan-based self-healing hydrogels with numerous amino groups; in addition, a large amount of chitosan-based single-net hydrogel with a reversible dynamic imine bond has been developed. Chitosan, also known as diacetyl chitosan, is the only natural alkaline polysaccharide that can be used to produce self-healing gel materials . Fu et aThe acylhydrazone bond is a Schiff base bond obtained by condensation of hydrazones with aldehydes (ketones) under weak acidic or weak basic conditions, which has better mechanical properties than the amine bond . As a dyDynamic covalent acylhydrazone bonds are very sensitive to environmental conditions, especially pH, and are unstable under acidic conditions; therefore, the rate of self-repair can also be regulated by pH . Under aThe Diels–Alder (DA) reaction is a cycloaddition reaction involving conjugated dienes and parodies and is one of the most efficient synthetic methods for forming unsaturated extranuclear rings at low temperatures to create complex natural products, while the reverse reaction occurs at high temperatures . Today, 3+-catechol, the mechanical, swelling and surface morphology of DN hydrogel can be controlled. The Diels–Alder click and coordination effect of smart hydrogen generated by Diels–Alder click and coordination guides the design of novel hydrogels with superior mechanical properties, self-healing properties and controllable crosslinking density , polymeric microspheres, dispersion particle gels (DPG) and other ground pre-crosslinking gels. The main advantages of prefabricated crosslinked gels are that they can accommodate pore and crack sizes and are not affected by solidification temperatures, salinity, and shear. Fan et al.  developeHydraulic fracturing in oil wells is a very important technological tool for reservoir modification and is the first method used to increase production from oil and gas wells. The main objective of this method is to inject fluids containing all types of proppants into the wellbore at high flow rates and pressures. Fracturing fluids are an important component of fracturing and need to be refined and improved to meet the requirements of complex fracturing reservoirs .Depending on the kind of polymer used, polymer-based fracturing fluids are divided into biopolymer, synthetic polymer and composite systems. Guar gum chains enable biopolymers to resist high-temperature environments through hydrogen bonding high molecular weight, extensive intermolecular linkage in aqueous solutions and the use of high-temperature resistant additives . Boric acid crosslinkers can be synthesized by optimizing the properties of boric acid and crosslinkers by adding ester bonds to the molecular structure and increasing the amount of boric acid to improve the stiffness and flexibility of the coordination bonds, resulting in a very-low-concentration rubber-shredding fluid that can withstand temperatures up to 175 °C, which can be used to solve the problems of conventional aggravated fracturing fluids such as large amounts of gum breakers and difficulties in rejection . Xiong eIn oil extraction, fracturing is a very important technology for low-permeability reservoirs. Because of the complexity of rock geology, the conventional acid fracturing fluid cannot satisfy the technical requirements for the exploitation of carbonate resources. A new type of acid gel fracturing fluid can be prepared under non-covalent interaction, which has ultra-high viscosity, excellent sand-carrying performance and good salt resistance and can maintain high viscosity and good rheology under high mineralization conditions and exhibit good high-temperature stability, which makes up for the shortage of conventional fracturing fluid performance at the present stage and can meet the needs of increasing production in unconventional low-permeability shale reservoirs . To impr3+ induces crosslinking reactions between different chains of the same molecule in weak gels, resulting in crosslinking polymer systems with localized mesh structures, which have a high resistance to flow but can still flow, thus allowing deep conditioning [With most mature oil fields entering the development stage of high water content and low oil production, injecting water-plugging agents into deep formations to expand the wave volume and chemically drive oil to improve recovery is the most common technical solution . Ordinaritioning . In the itioning  studied The deeper the oil and gas reservoir is buried, the higher the reservoir temperature, and the adaptive microgel (SMG) as a deep-seated dissection agent is limited by the complex pore structure and the strict matching of particle size. The poor mechanical properties and the non-degradable characteristics seriously limit its application. In response to some problems of current gel materials as chemical drives applied in oil fields, the main deficiencies are a too-fast water absorption rate, temperature resistance and poor long-term stability under high mineralization. Polymer nanoparticles are very flexible in controlling the blocking effect, and after high-temperature dissolution, the high-permeability layer was selectively blocked, and the low-permeability layer was hardly blocked, so polymer nanoparticles can be better applied to deep transfer drive technology . Liang eAs oilfield exploration and development continue, construction will be more difficult, especially for deep wells, complex wells and special process wells and for the development of special reservoirs, and with the importance of environmental protection, the requirements for drilling fluids are becoming higher and higher, and resistance to high-temperature, high-pressure, complex formations in deep wells, and oil and gas formation protection are still important directions for the development of drilling fluid technology. Conventional drilling fluids can adsorb to the clay surface through electrostatic attraction or hydrogen bonding to prevent clay swelling, but cationic polymers are incompatible with drilling fluids and are easily lost to adsorption to the clay particle surface, while ionic polymers are less resistant to high mineralization rates and have lower surface adsorption strength and are believed to provide superior pore consolidation in water-sensitive formations. Therefore, PH-responsive drilling fluids and high-temperature, low-temperature, high-pressure and environment-friendly drilling fluids need to be vigorously researched.With the development of oil and gas drilling to unconventional, ultra-deep and ultra-high temperature reservoirs, well leakage management will also become more and more difficult. The poor compatibility of common plugging materials with the formation and the weak mechanical properties of plugging gel cause a poor one-time plugging effect and easy-to-occur repeated leakage. Although the surface pre-crosslinking gel can be plugged with drilling, its low pressure-bearing capacity is not suitable for larger fracture strata, while the underground crosslinking gel can seal larger fractures, but it needs to stop drilling to seal, and the gel particles take longer time to become gel and delay the drilling period . TherefoSince the mechanical properties of the gel made by multiple hydrogen bonding crosslinking are strong, and there is reversible destruction and construction between strong and weak ionic bonds in electrostatic action; the multiple hydrogen bonding action and electrostatic action are introduced into the gel material, and the polymer chains are connected by physical entanglement, thus forming a three-dimensional structure with enhanced strength  between the dense structure of the crosslinking local area. After the gel particles enter the formation with drilling, the particles will be adsorbed in the formation fractures under the action of hydrogen bonding and electrostatic force, and with the continuous accumulation of gel particles, under the action of formation pressure, the gel particles will produce self-healing phenomenon after water absorption and expansion, so that the particles will condense into a block with high mechanical properties , which iOil and gas output may be increased by using a procedure called hydraulic fracturing. This process’s main objective is injecting the propellant slurry into the wellbore at high flow rates and pressures. This process can increase the permeability of the reservoir by fracturing the rock to form permanent fractures in the well . Deep unThe non-covalent interaction between hydrophobic association, hydrogen bonding and electrostatics provides the synergistic mixed-phase system with high-temperature resistance. The use of silica nanoparticles as the base point increases the physically crosslinking density by the crosslinking polymerization method, which leads to the rapid growth of the strength of the hydrogel and makes it a better and more stable three-dimensional structure. When the polymer gel enters the formation fracture after extrusion, the gel’s mechanical properties are improved and a dynamic reversible sol–gel phase transformation is produced, which can be used as a fracturing fluid material . TherefoWith the rapid development of the petroleum industry, oil and natural gas exploration has expanded and many fields have entered a medium to high development phase. Facing the serious problem of large channel development, when the water content of producing wells exceeds a certain amount, it has a great impact on the production of oil wells. Many large-scale dominant channels are formed in the deep part of the reservoir, resulting in serious inefficient and ineffective circulation, decreasing oil production and rising water content too fast in oil wells. Depth dissection technology is one of the most effective ways to improve the wave and volume of injected fluid and control inefficient and ineffective circulation ,87. The 3+, first hydrates to form a six-ligand hydration ion, then forms a double bond bridge ligand through hydrolysis and hydroxyl bridge, further polymerizes to form a multinuclear hydroxyl bridge complex ion, and then crosslinks with -COO- in the polymer polyacrylamide chain link to form a temperature- and salt-resistant gel structure, and silica nanoparticles are used to fill gaps in the 3D gel networks to keep them stable in high-salinity reservoirs, thereby maintaining their stability in high-salinity reservoirs, which has good temperature and salt resistance because of its special molecular structure and makes the crosslinking polymer system reversible and the viscosity recoverable by introducing acylhydrazone bonds. The use of polymer gel for water drive after conditioning makes the injection water flow into the low permeability reservoir preferentially, bypassing the gel barrier and increasing the injection wave area to achieve stable injection control, which effectively improves the injection development effect and increases the enhanced oil recovery Polymer gel material is a three-dimensional mesh structure formed by the polymerization of molecular chains through crosslinking. The classification of the crosslinking mode is divided into physically crosslinking gels with hydrophobic bonding interaction, hydrogen bonding interaction, electrostatic interaction and van der Waals interaction and chemically crosslinking gels with imine bonding interaction, acylhydrazone bonding interaction and Diels–Alder reaction. The reversible changes in the molecular structure of the gels through covalent or non-covalent bond interactions have strong mechanical properties. Therefore, environmentally responsive gels and self-healing gels can be developed for bioengineering, oil field development, artificial intelligence, etc.(2)Polymer gel materials are suitable for drilling fluids, fracturing fluids and improving the enhanced oil recovery. As oil fields are increasingly exploited at depth, more attention is paid to the development of high-temperature and salt-resistant polymer gels. Weak gels, plugging gels and drilling fluid materials such as well-wall reinforcing agents enter the formation with drilling and adsorb in the fractures and seal and strengthen the good wall after water absorption and expansion, extrusion and deformation and other external forces. Polymer gel is used as a fracturing fluid in the formation to create a seal by sol–gel conversion and increase the permeability of the formation. In the field of chemical oil drive, polymer gel increases the enhanced oil recovery by temporarily sealing the high permeability layer so that the injected water flows into the low permeability reservoir preferentially and increases the wave area of the injected water.(3)As oilfield extraction is increasingly moving to high-temperature and ultra-deep wells, the exploration environment is becoming more and more hostile, so the polymer gel materials needed also need to be temperature resistant, salt resistant and PH responsive and to possess other properties. We promote the development of an intelligent polymer gel and a higher-performance self-healing gel for malignant leak plugging, well wall stabilization and recovery enhancement work. In addition, we expand the application fields of polymer gel materials, develop scientific and intelligent working methods and promote the development of polymer gel materials in the direction of more intelligence.


Curcuma longa), has been widely reported for its diverse bioactivities, including against malaria and inflammatory-related diseases. However, curcumin’s low bioavailability limits its potential as an antimalarial and anti-inflammatory agent. Therefore, research on the design and synthesis of novel curcumin derivatives is being actively pursued to improve the pharmacokinetic profile and efficacy of curcumin. This review discusses the antimalarial and anti-inflammatory activities and the structure–activity relationship (SAR), as well as the mechanisms of action of curcumin and its derivatives in malarial treatment. This review provides information on the identification of the methoxy phenyl group responsible for the antimalarial activity and the potential sites and functional groups of curcumin for structural modification to improve its antimalarial and anti-inflammatory actions, as well as potential molecular targets of curcumin derivatives in the context of malaria and inflammation.Curcumin, one of the major ingredients of turmeric ( Plasmodium parasites infecting the human body, which are P. falciparum, P. vivax, P. ovale, P. malariae, and P. knowlesi. P. falciparum, identified as being the most common and feared species infecting humans, is transmitted into the circulatory system through the infective bite of female Anopheles mosquitoes. This triggers the development of sporozoites, which can rapidly infect liver cells within 30 min after a bite  have shown increasing drug resistance in malaria, where currently available antimalarial drugs such as artemisinin and chloroquine are no longer effective against malaria infection. In line with the primary goal of the Global Fund Strategy 2023–2028, sourcing and developing novel and potent inhibitors to combat drug resistance in malaria treatment is now a necessity, hence, the growing list of bioactive compounds, which are proven for their antimalarial activities  indicate that 1.7 billion cases of malaria have been recorded worldwide, which led to 10.6 million deaths within the previous 20 years (2000–2020) . Plasmodtivities  ,10639,1050 values, and percentage of schizont inhibition. Even though the pharmacological effects of curcumin have been attributed to the inhibition of multiple signaling pathways and enzymes in various biological systems, the detailed and specific molecular mechanism underlying its parasiticidal and anti-inflammatory activities remains to be elucidated in detail. The identification of specific target proteins for inhibition that correlates with the mechanism of action of curcumin and its various derivatives is essential for developing safer and more effective therapies against malaria.Various therapeutic effects of curcumin against a wide range of diseases have been extensively reported. Within the context of malarial infection, curcumin exhibits the dual antiplasmodial activity against parasites as well as anti-inflammatory action within the host. However, due to its limited bioavailability, the synthesis of derivatives with better pharmacological profiles than the parent curcumin through structural modifications has been explored. The incorporation of pharmacokinetic property prediction and structure–activity relationship data to rationalize the design of curcumin derivatives with enhanced bioactivity paves the way for the development of more potent antimalarial medicine. While preserving the methoxy phenol groups, which are crucial for the antimalarial and anti-inflammatory activities of curcumin, the reactive methylene group bridging the two carbonyl groups has been targeted for structural modification to generate promising curcumin derivatives. The presence of electron-withdrawing and electron-donating groups within the curcumin derivatives significantly affects their potency, pharmacokinetics, and physicochemical properties, as evaluated in terms of free binding energy, solubility, lipophilicity, IC


Sarcomas are a diverse group of malignant neoplasms of mesenchymal origin. They develop rarely, but due to poor prognosis, they are a challenging and significant clinical problem. Currently, available therapeutic options have very limited activity. A better understating of sarcomas’ pathogenesis may help develop more effective therapies in the future. The Sonic hedgehog (Shh) signaling pathway is involved in both embryonic development and mature tissue repair and carcinogenesis. Shh pathway inhibitors are presently used in the treatment of basal cell carcinoma. Its increased activity has been demonstrated in many sarcomas, including osteosarcoma, Ewing sarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, and malignant rhabdoid tumor. In vitro studies have demonstrated the effectiveness of inhibitors of the Hedgehog pathway in inhibiting proliferation in those sarcomas in which the components of the pathway are overexpressed. These results were confirmed by in vivo studies, which additionally proved the influence of Shh pathway inhibitors on limiting the metastatic potential of sarcoma cells. However, until now, the efficacy of sarcomas treatment with Shh pathway inhibitors has not been established in clinical trials. The reason for that may be the non-canonical activation of the pathway or interactions with other signaling pathways, such as Wnt or Notch. In this review, we present the Shh signaling pathway's role in the pathogenesis of sarcomas, including both canonical and non-canonical signaling. We also propose how this knowledge could be potentially translated into clinics. Signaling pathways, crucial in the physiological functions of cells and tissues, may, through dysregulation and ensuing dysfunctions, be significant factors in the process of tumorigenesis  pathway is a ligand-dependent signaling pathway. In vertebrates, three Hh ligands have been described: Desert hedgehog (Dhh), Indian hedgehog (Ihh), and Sonic hedgehog (Shh), the last one being the primary ligand in humans  and long non-coding RNA (lncRNA)  have been shown to promote osteosarcoma cell growth by activating the Hh pathway. Hsp70 and CD63 expression has been detected in these exosomes  or vismodegib (Smo inhibitor) decreased osteosarcoma cell migration. Moreover, inhibition of the osteosarcoma metastasis to the lung was observed during this combination treatment  and, as a result, induces chromatin condensation and transcriptional repression  is the second most frequent bone sarcoma affecting children and adolescents  in the treatment of chondrosarcoma in primary xenografts was also evaluated. The observed effects included decreased volume and cellularity of the tumor, tumoral calcification, and decreased chondrocyte proliferation. The high efficacy of the treatment was likely due to the ligand-dependent nature of the Hh pathway present in chondrosarcoma  via mTOR/S6K1 signaling pathway  treatment in the embryonal subtype of rhabdomyosarcoma (ERMS) with a mutation in Leiomyosarcoma (LMS) is one of the most common soft-tissue sarcoma in adults, representing 10–20% of newly diagnosed cases. Uterine LMS is, in turn, the most common type of uterine sarcoma  are a group of mainly soft-tissue cancers which most commonly develop in the kidney or the brain but can be found in any body part. Those located in the brain are referred to as atypical teratoid/rhabdoid tumors (ATRT). MRT mainly affect infants and is believed to develop during embryogenesis. They remain one of the most lethal pediatric cancers and have a particularly bad prognosis in case of metastases , and sonidegib . While cyclopamine is chemically unstable . There is an ongoing discussion about their potential therapeutic value, particularly in the case of the non-canonical activation of the Shh pathway, where Smo inhibitor therapies have no effect  is a natural anthraquinone derivative which has been reported to have various desired pharmacological effects, such as an anti-neoplastic, antioxidant, anti-inflammatory, and anti-apoptotic potential , a substance extracted from the plant Dysregulation of the Hh pathway, leading to overexpression of its components, has been observed in some sarcomas, including osteosarcoma, Ewing-sarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, and malignant rhabdoid tumor. This points to the critical role played by the Hh pathway in the tumorigenesis of sarcomas; however, Hh components do not seem to be the core mechanism in the pathogenesis of these tumors. Subsequently, the efficacy of sarcomas treatment with Hh inhibitors was assessed. Both in vitro and in vivo studies showed promising results, but they have not yet been confirmed in clinical trials. We believe that the observed lack of effect may result from two factors: the non-canonical pathway activation and interactions with other signaling pathways.Due to its mechanism, the non-canonical pathway activation is less sensitive to Smo inhibition, making Gli inhibitors the only potentially effective Hh-targeted treatment in this group of sarcomas. Understanding which mechanism of Hh pathway activation is present in each sarcoma subtype Table  is cruciAnother aspect of the Shh pathway’s role in the treatment of sarcomas is its possible influence on susceptibility to immunotherapy. Reduced expression of the Hedgehog signaling pathway and the presence of CD8 + T cells have been shown to correlate with the best clinical outcome in patients with sarcoma undergoing immunotherapy (D’Angelo et al. A better understanding of the role played by the Shh pathway in the pathogenesis of sarcomas is necessary. We propose that further research should primarily focus on the role of the non-canonical pathway and the development of its inhibitors as well as potential combination therapies, which would simultaneously target not only the Shh pathway but also other signaling pathways. Such studies could be used to develop an effective therapy for sarcomas connected with Shh pathway dysregulations and thus help solve one of oncology’s most prominent problems (see Table


The COVID-19 sequelae have been shown to affect respiratory and cardiological functions as well as neuro-psychological functions, and, in some cases, metabolic/nutritional aspects. The Italian National Institute for Insurance against Accidents at Work  recorded that, until December 2022, 315,055 workers were affected by COVID-19; therefore, there is a need to identify an effective approach to treat such patients. Robotic and technological devices could be integrated into the rehabilitation programme of people with long COVID conditions. A review of the literature showed that telerehabilitation may improve functional capacity, dyspnoea, performance, and quality of life in these patients, but no studies were found evaluating the effects of robot-mediated therapy or virtual reality systems. Considering the above, Fondazione Don Carlo Gnocchi and INAIL propose a multi-axial rehabilitation for workers with COVID-19 sequelae. To accomplish this goal, the two institutions merged the epidemiological information gathered by INAIL, the expertise in robotic and technological rehabilitation of Fondazione Don Carlo Gnocchi, and the literature review. Our proposal aims to facilitate a multi-axial rehabilitation approach customized to meet the unique needs of each individual, with a particular emphasis on utilizing advanced technologies to address the current and future challenges of patient care. On 11 February 2020, the WHO announced the name of the respiratory disease caused by the new coronavirus: COVID-19. This novel coronavirus disease, induced by the virus SARS-CoV-2, originated in Wuhan (China) and spread worldwide, with 757,264,511 confirmed cases and 6,850,594 deaths (WHO update on 21 February 2023). Since the start of the COVID-19 outbreak in China, and its subsequent global spread, Italy has been one of the first countries to be affected, and the number of recorded cases has been among the highest in the world: after 3 years from the beginning of the pandemic, 25,547,414 confirmed cases and 187,850 deaths  were reported.On 9 March 2020, in response to the increase in COVID-19 cases in the country, the Italian government decreed urgent measures promoting social distancing to limit the spread of the virus. From 11 March 2020, all non-essential activities, such as social interaction, sporting, retail, and recreational activities, were suspended and, when possible, most organisations had to introduce remote work: citizens were forced to stay at home and travelling was drastically reduced. People’s freedom to move was limited to urgent health circumstances or working in healthcare. A similar lockdown was never experienced before in Italy. Due to the rapid spread of the virus, China first and later Italy, went into a full lockdown, while other countries used social distancing as the principal strategy to limit the number of new cases .,51.50,51During the pandemic, FDG continued to provide rehabilitation services to patients, particularly by utilising various technologies. In order to accomplish this, FDG acquired a specific CE-certified platform that gave it access to patients’ homes throughout the lockdown. Following a training period, telerehabilitation sessions based on real-time interactivity (synchronous interaction) now make up around 10% of outpatient rehabilitation sessions .In the last year, Fondazione Don Carlo Gnocchi has started a collaboration with INAIL to ensure multi-axial rehabilitation for COVID-19-affected workers integrated with technological and robotic devices. The protocol is characterised by a multiperspective approach, including respiratory and cardiological rehabilitation, neuromotor rehabilitation, cognitive rehabilitation, and psychological and nutritional treatment. The protocol takes advantage of the beneficial effects of robotics and technologies on sensorimotor and cognitive deficits , which h2) in the recovery phase, and the reduction of the level of lactates produced during the exercise and the share of non-metabolic carbon dioxide (CO2) generated by the bicarbonate system to buffer its effects on pH.Respiratory function rehabilitation can be divided into two main categories: (a) rehabilitation of respiratory function and motor performance; and (b) bronchial disruption. The rehabilitation of respiratory function and motor performance involves motor training exercises aimed at improving the strength and endurance of the upper and lower limbs, and respiratory muscles ,54,55,56Bronchial unblocking is an important element of rehabilitation, especially for patients with hypersecretion. The techniques used are: (a) the Active Cycle of Respiratory Techniques (ACBT), (b) the Total Slow Expiration with Open Glottis in Lateral Decubitus (ELTGOL) , (c) theFinally, the continuation of such a rehabilitation programme, also through telerehabilitation and the telemonitoring of heart rate, body pressure, pulse oximetry, and hemoglobin, is recommended since exercise’s physiological effects decay if ad hoc maintenance programmes are not provided .Therefore, we suggest using the following outcome indicators to assess the impact of respiratory and cardiovascular rehabilitation: Barthel Dyspnoea ; InstrumThe focus of neuromotor rehabilitation in patients with SARS-CoV2 outcomes relies on the treatment of the central and/or peripheral nervous system. Neuromotor rehabilitation should be performed by the integration of conventional techniques with new advanced technological and robotic devices.Conventional rehabilitation allows for functional recovery of the limbs and trunk, as well as the complex tasks that rely on them, such as walking, balance, coordination, and postural control, all with the goal of regaining autonomy in ADLs.sensorimotor training to restore normal motor programmes, maintain joint mobility of the upper and lower limbs through passive and active assisted mobilisations, and control pain when present;programmes to increase muscle strength and endurance with incremental exercises appropriate to the patient’s performance, and programmes to recover segmental movement and then more complex motor functions;training from autonomies in safe postural transitions;postural control exercises;balance and gait recovery exercise;activities aimed at family and social reintegration.Conventional re-education protocols should promote:intensified treatment;objective measurement of patient’s achieved goals;personalised treatment;stimulation of neurocognitive as well as motor aspects .The robotic device can be:End-effector, where movements are generated from the most distal segment of the extremity and no alignment between patient-robot joints is required ;Exoskeletons, which have a one-to-one correspondence between robots and human joints, and every single joint is guided along a pre-programmed trajectory . They caThere are also advanced technological systems such as:sensor-based systems that allow for the control of a wide range of limb movements, but also of the whole body in space (trunk and gait control);electromechanical systems with body weight or a district relief system .Robotic rehabilitation grants treatment of both upper and lower limbs; they represent an innovative rehabilitation approach that allows:Robotic rehabilitation approaches can be useful for post-COVID symptoms such as disabling balance disorders and insufficient postural control using technological/robotic platforms. Stabilometric and dynamic platforms can also be used to carry out evaluative tests of the patient’s condition before and after the rehabilitation course. Moreover, exoskeleton or end-effector robotic devices (that safely sustain the patient using a weight body support system), allowing more physiological gait training even in the presence of a significant lower limb strength deficit, can be utilised for gait training.Some sensor-based and robotic devices have been found to be useful not only for inpatient rehabilitation, but also for home-based rehabilitation with caregivers (telerehabilitation and telerobotic programmes through telemonitoring). Through monitoring systems, the therapist evaluates the patient’s progress every day and adjusts the treatment to suit the patient’s needs. Furthermore, telerehabilitation systems also allow for the simultaneous treatment of patients in small groups.We propose to evaluate the effects of neuromotor rehabilitation using the following outcome measures: the Motricity Index , to evalLong COVID’s cognitive deficits, including memory loss and moderate cognitive impairment, might last for months after recovery from the acute phase.The most experienced symptoms are memory issues, multitasking deficits, processing speed reduction, and lack of concentration. These symptoms can prevent the return to normal activities of daily living. Therefore, early interventions aimed at limiting short- and long-term damage are important.It is first necessary to conduct a neuropsychological assessment to highlight cognitive deficits and to characterise their impact on the individual, with the aim of guiding treatment. The assessment will be carried out through a cognitive screening test and, later, with specific tests for individual impaired areas.Therapy will be trained according to the functions that are most affected on the assessment tests or experienced as more debilitating by the patient.targeted reinforcement of deficit skills through the administration of exercises of increasing difficulty ;adaptation of the patient’s behaviours and setting of new compensation strategies ;evaluation of the results obtained is mandatory at the end of the treatment.Cognitive training aims at reducing symptoms and will be based on two works conducted in parallel:The first interview and administration of standardised tests should be conducted in person; cognitive training could be conducted in telerehabilitation or mixed regimens.To assess the effectiveness of the proposed intervention, we suggest using the following outcome measures: the Montreal Cognitive Assessment , to evalThe lockdown has had an impact on the whole community, particularly in terms of family separation. This mostly affected those who were unable to spend the lockdown period with their family. This situation led to psychological distress for patients .To overcome this distress, adequate psychological support for patients should be conceived, and “technological” solutions, such as video calls, can be used to maintain communication with patients also after dismission.In this context, we suggest evaluating anxiety, using the Beck Anxiety Inventory , depressSignificant weight loss (sarcopenia) is common in post-COVID patients with severe symptoms due to a long ICU stay. Nutritional problems need particular attention as they result in reduced exercise tolerance, susceptibility to infection, and consequently, to worse outcomes. Therefore, it is critical to employ nutritional status screening tools to identify people who are malnourished or at risk of malnutrition, and to implement procedures to monitor and regulate daily nutritional intake in these circumstances.For this purpose, we propose the use of the Mini Nutritional Assessment  to gradeScientific evidence shows that SARS-CoV-2 infection may affect health at different levels. Due to the COVID-19 multi-organ sequelae, an exceptional number of workers underwent medical complications and lost their ability to work and conduct a normal life.In this scenario, INAIL plays a crucial role in supporting Italian Public Healthcare to guarantee optimal multi-axial rehabilitation assistance in terms of the appropriateness and timeliness of the rehabilitation intervention.As an institution created to protect and support workers’ wellbeing integrity, INAIL provides multi-axial rehabilitation assistance for those whose health has been affected by the occurrence of the pandemic. These services are dispensed in specific rehabilitation institutes such as the centres of Fondazione Don Carlo Gnocchi. These centres provide professional expertise, specialised structures, and up-to-date technologies, such as robotic devices, to rehabilitate different types of disabilities.An optimal multi-axial rehabilitation approach entails stepping in at the earliest stages of the pathological process to promote healing and improve clinical conditions, selecting the most effective treatments according to scientific evidence and good practice. This requires including multiple district interventions and contemplating the possibility of repeated treatments (deferred rehabilitation).As we described in this proposal, both conventional and technological treatments should be applied. Technological-robotic rehabilitation has been proven to be a promising tool for both physical therapy and remote interventions (telerehabilitation).Telerehabilitation has expanded its applications in the last 25 years due to the development of new computer science technologies . During Whatever type of treatment we intend to apply, reintegrating workers with long COVID sequelae in their jobs, families, and social activities will undoubtedly have a positive impact on their quality of life and will reduce the costs related to days of absence from work and medical complications. In order to ascertain the efficacy of the proposed treatment in individuals experiencing long COVID outcomes, it is imperative that experimental studies be undertaken. These investigations should involve rigorous clinical trials or observational studies, employing robust methodologies and a sufficiently large sample size. By carefully monitoring the response of patients to the treatment, including its impact on various symptom domains, functional outcomes, and quality of life measures, researchers can acquire valuable evidence regarding its effectiveness. Additionally, long-term follow-up assessments should be conducted to evaluate the durability and sustainability of the treatment outcomes over an extended period. The findings derived from such studies will not only contribute to the existing body of knowledge, but also provide crucial insights for healthcare professionals and policymakers when determining the most appropriate therapeutic interventions for patients suffering from long COVID.The COVID pandemic has been a hard test for Italian healthcare professionals, patients, and their caregivers in terms of the management of the medical emergency and because of the human and social costs that it caused to the country.Aside from providing adequate assistance to the affected workers, our proposal intends to promote a multi-axial rehabilitation approach that is effectively tailored to individual needs, taking into special account the availability of advanced technologies and their application to the current and future challenges of patient care.


The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods. Whenever cells in the brain begin to multiply uncontrolled, a mass-like formation forms, known as a brain tumor . PatientArtificial intelligence (AI) and machine learning (ML) ,4,5 haveDeep neural networks (DNNs) have deep learning, which has revolutionized different areas, such as agriculture ,10,11,12In the context of brain tumor detection and diagnosis, DNNs can analyze large volumes of medical imaging data and automatically identify patterns and features that are indicative of brain tumors. These features may include the shape, size, and location of the tumor, as well as its relationship to surrounding brain tissue. One of the key advantages of DNNs is their ability to learn from vast amounts of data. By training on large datasets that contain both tumor and non-tumor images, DNNs can learn to identify subtle differences between the two and accurately distinguish between them. This ability to learn from large amounts of data also makes DNNs more robust to variations in imaging conditions, such as differences in scanner hardware or patient positioning. Another advantage of DNNs is their ability to incorporate multiple imaging modalities into their analysis. For example, a DNN can integrate information from medical images to improve its accuracy in detecting and diagnosing brain tumors. Overall, DNNs are effective in brain tumor detection and diagnosis because they can automatically learn and extract features from large volumes of brain medical imaging data, and they can integrate information from multiple imaging modalities to improve their accuracy.CNNs are designed to process images and can automatically learn and extract features from brain imaging data to enhance their accuracy in detecting and diagnosing brain tumors . The feaThere have been many attempts carried out by researchers to improve the accuracy of identifying brain tumors. In assessing the clinical importance of each MR sequence in brain tumor prediction and detection, the prior studies are highly helpful and are a great resource. Imaging modalities, such as MRI and CT, were utilized by the researchers in order to discern between tumors and other abnormalities. MR spectroscopy has been employed for the categorization of brain tumors in a wide variety of studies. In particular, spectroscopic and magnetic resonance imaging were used in a study; Wang et al.  tried toDevnath et al.  proposedEnsemble model with convolutional features: The research proposes an ensemble model that combines convolutional features extracted from a specialized convolutional neural network (CNN). The ensemble model incorporates a voting mechanism, employing logistic regression and a stochastic gradient descent classifier to generate a final prediction.Comparison of convolutional features: The effectiveness of models utilizing convolutional features is compared to the impact of using the original characteristics of the data. This analysis provides insights into the benefits and performance improvements offered by the convolutional features in the context of brain cancer forecasting.Evaluation of multiple ML models: The study evaluates the performances of various machine learning (ML) models, including Random Forest (RF), K-Nearest Neighbor (k-NN), decision tree (DT), and Extreme Gradient Boost (XG-Boost). The results of these models are compared to assess their effectiveness in the classification task.Ensemble model experimentation: Among the evaluated ML models, the three best-performing models are selected for further experimentation as an ensemble model. This ensemble approach aims to leverage the strengths of multiple models to improve the classification accuracy of the system.Performance comparison with other ML methods: The proposed framework’s performance is assessed in terms of accuracy, precision, memory usage, and the F1-score. The evaluation includes a comparison with other ML methods to determine the superiority of the proposed framework in the context of brain cancer forecasting.This study introduces a novel system that offers several advantages in the context of brain cancer forecasting. The main contributions and characteristics of the proposed system are as follows:By employing an ensemble model with convolutional features and conducting a comprehensive evaluation of various ML models, this work provides insights into the effectiveness and performance of different approaches for brain cancer forecasting. The proposed framework’s performance is thoroughly assessed and compared to other ML methods, providing a comprehensive analysis of its capabilities.In this section, the proposed framework for brain tumor prediction is discussed using the MRI dataset, as shown in The convolutional operations of CNN are initially utilized in order to extract the features. In addition, a dimensionality reduction method is then used to pick a limited collection of effective features for classification in order to increase the generalization ability and performance of the classifier. This is performed in order to make the classifier more efficient. The dataset and the data will be discussed in this part of the article.After that, the pre-processing of the data, feature extraction, and dimensionality reduction are detailed, and then the classification algorithms that were employed are presented.This study made use of the “Brain tumor” dataset, which is publicly available on figshare . The datConventional ML algorithms can be effective in detecting brain tumors from imaging data by learning patterns from large datasets. Feature extraction and selection are critical steps in developing a successful machine learning model, and various approaches can be used to identify relevant features for training the model . AlgoritThere are various approaches to feature selection in ML algorithms for brain imaging. One common approach, such as PCA, is to reduce the dimensionality of the data and extract relevant features . AnotherAfter pre-processing, the number of features obtained was more than 23,000 for a set of 3712 brain MRI images. Therefore, optimization was performed using PCA to overcome the curse of dimensionality, removing data inconsistencies and redundant data and finding correlations among different features to create a new data space with fewer features that retain most relevant features. After using PCA, 12,000 features were selected to reduce the dimension for better performance. The stepwise functionality of PCA is represented in The following machine learning models were explored as part of this work to perform analyses for improved brain tumor prediction using MRI data.Ada-Boost is one of the earliest ensemble-boosting algorithms . The weiXG-Boost is an extremely effective and adaptable method for distributed gradient boosting. XG-Boost is a framework for gradient boosting that integrates machine learning classifiers . It emplAltman first introduced the non-parametric technique for data classification in 1991 . InitialIn the category of non-parametric supervised learning is the decision tree classifier. The decision node and the root node are the two branches that emerge from the node to serve the inner node . The leaFriedl et al.  created Ensemble is the idea of wrapping up the basic models to make the average prediction, which is more reliable in comparison with a single prediction with single models . It workThis study applied an ensemble Ada-Boost, XG-Boost, and Random Forest (XG-Ada-RF), based on their individual performances for the binary classification of brain MRIs for predicting brain tumors. The detailed architecture of the proposed ensemble XG-Ada-RF model is shown in Algorithm 1: Procedure Ensemble Model , was proposed. The ensemble approach aimed to leverage the strengths of these individual models while mitigating their weaknesses. The ensemble took advantage of the diverse behaviors and decision-making processes of the constituent models, resulting in more robust and accurate predictions. The proposed ensemble approach achieved a high level of accuracy in binary class classification for detecting brain tumors, and the results validated the significance of the ensemble approach.Furthermore, we compared our study with the techniques used in previous studies, as shown in This paper provides a comprehensive review of the research on the detection and classification of brain tumor MRI images using ML algorithms, specifically focusing on the use of an ensemble of the three best-performing models. Through a critical analysis of conventional ML models, this study offers a detailed examination of their strengths and limitations. The proposed ensemble model, XG-Ada-RF, demonstrates superior predictive performance, with an accuracy of 94.9% for the healthy class and 95.9% for the tumor class. This outcome highlights the effectiveness of the ensemble approach in accurately predicting and classifying brain tumors from medical image data. Building upon this research, a comprehensive framework based on an ensemble of three classifiers was developed to achieve accurate predictions and classifications. This framework serves as a valuable tool for enhancing our understanding and expanding the scope and significance of brain tumor classification.Furthermore, an experimental comparative performance analysis was conducted to assess the robustness of the various algorithms and the ensemble model. Key evaluation metrics, such as accuracy, sensitivity, specificity, and log loss, were considered to provide a comprehensive evaluation. The diverse results obtained by implementing the aforementioned algorithms on the given dataset further supported the objectives of this study.Looking ahead, future research directions could focus on refining and optimizing the ensemble model, exploring additional ML algorithms, and incorporating advanced techniques, such as deep learning. Additionally, further investigation into the interpretability and explaining ability of the ensemble model could provide valuable insights for clinical decision-making. Overall, the results of this study emphasize the superiority of the ensemble approach in brain tumor classification, laying a foundation for further advancements in this field.


This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability. With the development of modern industry, machine health monitoring becomes more and more important to maintain the safe operation of modern mechanical equipment ,2. BeariIn recent years, the compound fault diagnosis of rolling bearings has received increasing interest and attention from researchers. The research methods for compound fault diagnosis mainly include compound fault mechanism research ,11, the However, there are some issues, such as mode aliasing and the end effect, that can directly affect the accuracy of a compound fault diagnosis. With the rapid development of artificial intelligence techniques, machine learning methods, including support vector machines (SVMs), Bayesian classifiers, artificial neural networks (ANNs), and convolutional neural networks (CNNs), have already been applied as potential tools to fault diagnosis, especially deep learning approaches. Ref.  proposedThe above models demonstrate that deep learning approaches have significantly improved fault diagnosis effectiveness. However, the relationship between compound fault and single fault is one-to-many or many-to-many, which is not a simple superposition of a single fault signal. Compared with signal faults, feature extraction and location of compound faults are more difficult and challenging, which brings great difficulties to compound fault diagnosis. Currently, intelligent diagnosis methods based on deep learning mainly focus on single faults. Some models are suitable for single fault diagnosis; however, they are not effective for compound fault diagnosis. The research and application of deep learning approaches to bearing compound fault diagnosis are still in their infancy. Thus, in this study, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) was proposed. A novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed to extract features adaptively from a raw signal.The modified activation concatenated ReLU (CReLU) is applied in the shallow layer of GDDCNN. It can improve the performance of global feature extraction.The global max pooling (GMP) strategy is designed to replace the traditional fully connected layer, which can extract shift-invariance features and reduce model parameters. It overcomes the overfitting problem in the training process.The proposed GDDCNN model is a deep convolutional neural network that incorporates both global feature extraction and detailed feature extraction, where G means global feature extraction, D means detailed feature extraction, and DCNN represents a deep convolutional neural network. DCNN is a feature-progressive learning algorithm in which the deep network can continue to learn more advanced fault features based on shallow features. By designing two feature extraction modules, G and D, better learning ability can be achieved for DCNN. Therefore, more abundant fault features can be extracted through GDCNN, thus improving the performance of fault diagnosis. The contributions of this study are summarized as follows:The rest of this study is described as follows. CNN can be built with multiple layers, comprising a convolution layer, a pooling layer, an activation layer, and a classification layer. The convolution layer, pooling layer, and activation layer are used to extract features from input signals. One-dimensional CNN has been widely applied to 1-D vibration signal processing due to its powerful feature extraction ability. The classification layer applies the extracted features to classify. l-th layer; i-th convolution kernel of the l-th layer; l-th layer; the notation The convolution layer is the core layer of the CNN structure, which convolves the input data with filter kernels. The network makes the filter learn to activate when it extracts certain features, then realizes feature extraction. The mathematical form can be described as follows:LU) .LU represents the ReLU activation function. The modified activation CReLU is used in the shallow (first and second) convolution layers, which can improve the performance of global feature extraction.Modified activation operation: In addition to the convolution, the activation operation also affects the performance of CNN. Due to the advantages of simple calculation and no vanishing gradient problem, ReLU is widely applied in CNN as an activation unit. However, when the inputs are negative, the neurons are always inactive. These dead neurons in a network may never activate, which stops learning and thus affects the learning ability of the network. In , it was GMP strategy: The fully connected layer is generally applied after the last convolutional or pooling layer, which can integrate the class-differentiated local features extracted by CNN. Each neuron in the fully connected layer is fully connected to all the neurons in the previous layer. Due to its fully connected characteristics, the parameters of the fully connected layer are numerous, and the calculation is extremely complicated. The right part of p(x) and q(x) represent the target distribution and estimated distribution, respectively. The loss function can be expressed as follows:The architecture of GDDCNN is designed to take advantage of one-dimensional CNN. The cross-entropy loss function is used to estimate the consistency between the Softmax output probability distribution and the target class probability distribution. Suppose that The gradient of loss L and the gradient of parameters related to BN should be back-propagated in GDDCNN during training as per the following rules:Adam is a learning rate adaptive optimization algorithm that assembles the Adagrad algorithm and the RMSProp algorithm, which allows a model to allocate more updates to rarely occurring features, thereby aiding in the convergence of the optimization process. By bringing together the adaptive learning rates of Adagrad and the stability of RMSProp, the Adam algorithm provides an effective solution to optimize the proposed model, enabling it to converge quickly and efficiently while avoiding common optimization pitfalls. The choice of optimizer plays a crucial role in the success of the proposed model’s training process.In order to minimize the value of the loss function, the Adam optimization algorithm is applied to update the weights and obtain the optimal weights. The Adam optimizer is chosen for the following reasons: Bearing vibration signals typically contain a large number of data points due to their time-series nature. The efficient convergence property of the Adam algorithm allows the model to learn from a large volume of data more quickly. This, in turn, accelerates the assessment of the bearing’s health status, saving training time. Features in bearing vibration signals may vary in importance over different time intervals. Adam’s adaptive learning rate automatically adjusts the learning rate based on the gradient of each parameter. This capability helps the model better adapt to dynamic variations within a signal. When the Adam optimizer is initialed, it is necessary to set the learning rate The raw vibration signals of rolling bearings are collected by a data acquisition device.The obtained vibration signals are sliced into samples of length 2048 for standardization processing and then used as network input.Extracting fault features by combining global feature extraction with detailed feature extraction. Utilizing the GMP layer to integrate features. Then, Softmax is employed as a classifier to classify fault features.Testing samples are entered into the network to realize fault diagnosis and validate the performance of the proposed model, GDDCNN.In this study, the two-stage gear drive test bench shown in In addition to the data obtained from the above two-stage gear drive test bench, this study also utilized the public Case Western Reserve University (CWRU) dataset  to simul(1) Sample Segmentation: After data acquisition, an easy data augmentation technique of slicing the raw vibration signals with overlap is applied to process training samples. In order to reflect the periodicity of raw signals, this study set the sampling size to 2048 and the overlapping window size to 512. The data segmentation process is shown in (2) Standardization: After sample segmentation, in order to improve the convergence speed and accuracy of the model, the samples need to be standardized. The samples are standardized by the z-score method and follow the distribution of the mean value 0 and variance 1. The standardization process for the raw data , and the remaining activation functions are ReLU. To improve the performance of the proposed model, GDDCNN, BN is applied after each activation layer. Each convolution layer and pooling layer utilizes a zero-padding of causal and same type to prevent the loss of edge information. The detailed parameters of the proposed network are listed in In order to validate the feasibility of the proposed method, which is compared with several currently popular models, including classical CNN, CNN-SVM, and WDCNN . This stTP represents the number of true positive samples, i.e., the number of samples correctly predicted as positive by the classifier; TN represents the number of true negative samples, i.e., the number of samples correctly predicted as negative by the classifier; FP represents the number of false positive samples, that is, the number of samples incorrectly predicted as positive by the classifier; and FN represents the number of false negative samples, that is, the number of samples incorrectly predicted as negative by the classifier.Accuracy is the most common metric used to evaluate the performance of a classifier. It calculates the proportion of correctly classified samples out of the total number of samples. Specifically, accuracy can be calculated using the following formula:The results of the diagnosis accuracy for each category using four different methods are summarized in In order to demonstrate the advantages of the proposed method more visually and evidently, a line chart is used to compare the accuracy of the four methods, as shown in Precision is similar to but different from accuracy, which is the proportion of correctly predicted positive samples among all samples identified as positive. Precision represents the prediction accuracy of the positive sample results. Recall refers to the proportion of correctly predicted positive samples among all true positive samples, which reflects the proportion of positive samples correctly predicted. F1-Score is the harmonic mean of precision and recall, which combines information from these two metrics. The calculation of precision, recall, and F1-score is as follows:Precision, recall, and F1-score are also crucial performance metrics for a classifier; a higher precision, recall, and F1-score indicates a better performance of the classifier. It can be observed from N is the number of testing samples, the value of In order to further validate the effectiveness of the fault diagnosis model, three error metrics—maximum average error (MAE), mean squared error (MSE), and root mean squared error (RMSE)—are selected to evaluate the fitting effect of the fault diagnosis model. The formulas for each metric are defined as follows:Finally, the feature distribution in different layers of the proposed model, GDDCNN, can be visualized through the t-SNE technique. t-SNE is a nonlinear dimensionality reduction algorithm that calculates the positions of sample points based on the similarity between sample points in high-dimensional space and the distances between sample points in low-dimensional space. The experiment results, i.e., the position information after dimension reduction of the raw signal, six convolutional layers, and the GMP layer, are shown in In order to further verify the effectiveness and performance of the proposed model, GDDCNN, fault diagnosis experiments are carried out with the simulated data from the CWRU dataset. Data is processed by overlapping sampling during simulation. The distribution of samples is listed in The average accuracy of the four models is shown in This study proposed a novel deep CNN named GDDCNN for the diagnosis of practical engineering compound faults. GDDCNN extracted robust features adaptively from raw vibration signals and realized compound fault diagnosis without any manual processing. The datasets acquired by a two-stage gear drive test bench and simulated with the published dataset of CWRU were applied to experiments to verify the performance of the proposed model. The experimental results demonstrate that GDDCNN can achieve more than 98% accuracy on both datasets. Compared with three other existing CNN-based models, this model not only achieves satisfactory diagnostic accuracy but also exhibits strong stability. By combining different convolutional kernel sizes, improved activation CReLU, and GMP strategy, the performance of GDDCNN has been improved, and compound fault diagnosis has been achieved more effectively. In further work, we will continue to explore how to adaptively select hyperparameters to enhance the accuracy and stability of the diagnosis model. We also aim to research the application of transfer learning to achieve compound fault diagnosis across different operating conditions.


Frontiers in Physiology-Renal Physiology launched a recent Call for Papers on Sex differences in renal physiology. Regarding this Research Topic, computational models of sex differences in epithelial water and solute transport along the nephrons and identification of risk and prognosis factors associated with acute kidney injury (AKI) in pregnant patients are very attractive and promising.The major functions of kidneys are the regulation of blood pressure, excretion of metabolic wastes, and regulation of electrolytes and acid-base balance. There are well-established sex differences in renal function across many species, including humans and rodents. In fact, many studies have suggested the female sex as a protective factor in the progression of many renal diseases, both clinically and in experimental animal models. However, the influence of biological sex on renal disease development and progression remains poorly understood. As such, there continues to be a high degree of interest in better understanding the mechanisms controlling renal function under both physiological and pathophysiological conditions in both sexes. To that end, Stadt and Layton developed the first sex-specific computational models of epithelial water and solute transport along the nephrons from male and female mouse kidneys. These models can serve as a valuable tool for the analysis of findings related to genetic modification from experiments conducted in rats and mice. These models can be useful for the interpretation of the differences in the abundance of apical and basolateral transporters, glomerular filtration rate, and tubular dimensions between the sexes. Using their computational models, the authors showed that the mouse kidney proximal tubules reabsorb less Na + than rats, with Na + reabsorption higher in males than females in both species. They also proposed that the females exhibit enhanced water and Na + transport along distal nephrons in both species due to the abundance of key Na + transporters resulting in similar urine excretion between the sexes. Therefore, this model can be applicable to all mammalian kidneys, irrespective of species to enhance the understanding of physiological and pathological mechanisms for the treatment of diseases without introducing intended genetic manipulation from compensation.Monteiro et al. reported metabolic and renal changes due to fructose overload introduced after weaning in rats, with greater effects in males than females. The authors showed that fructose intake was associated with increased blood pressure, body weight, plasma triglycerides, renal macrophage infiltration, decreased sodium, and potassium excretion, and decreased renal eNOS expression in both male and female rats. However, the excretion of sodium, potassium, and magnesium was higher in females. They further showed that males receiving fructose had a reduced glomerular filtration rate compared to females. These observations indicate that nutritional status in the early developmental stage is crucial in an individual’s health and improper dietary changes in early life could become a cause of non-infectious diseases in the future, particularly in males.Bajwa et al. examined the role of the receptors for advanced glycation end products (RAGE) that interact with multiple pro-inflammatory/pro-oxidative ligands and are considered to have potential pathophysiological effects on the kidneys. They reported that the RAGE mRNA expression in the kidney cortex of old (>70 weeks) wild-type female mice was higher than that in males of similar age. The expression for macrophage scavenger receptors (MSR) that mediates the endocytic uptake of AGEs in vivo in the kidneys also increased in aged mice, irrespective of sex and genotype (wild-type and RAGE knockout), and this increase in MSR correlated with the increase in AGE content. Interestingly, there was an age-dependent increase in renal damage (NGAL and KIM1) associated with fibrosis, hyaline casts deposition, and accumulation of immune infiltrates comprising mostly of B220-positive B cells, and some B-1a (CD19+ CD43+ CD5+) in RAGE knockout (KO) mice, irrespective of sex. However, there was an age-based increase in pro-fibrotic and pro-inflammatory markers  in RAGE-KO male mice. These observations suggest that the male sex predisposes towards premature aging-dependent renal damage irrespective of RAGE.Wang et al. identified the risk and prognosis factors associated with acute kidney injury (AKI) in pregnant patients with hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome. Using data from 110 pregnant HELLP patients with or without AKI, they reported the lowest hemoglobin and highest bleeding incidence as independent risk factors for AKI onset and infection as independent risk factors for maternal mortality. Serum creatinine was reported to be the independent risk factor for both AKI onset and maternal mortality. Moreover, these were different based on the severity of AKI. Thus, the findings of this study support the present view of the diagnosis and treatment of HELLP and AKI patients.The goal of this Research Topic was to identify sex-specific differences in renal function to promote further understanding of physiological differences between male and female kidneys in animal models and in humans, to improve current therapies as well as provide opportunities for the development of novel protective strategies in nephrology. This Research Topic was also designed to stimulate thought and interest among the renal research community as they consider sex as a biological variable in future research projects.


Cirrhosis is the end result of liver fibrosis in chronic liver diseases. Studying the mechanisms of its development and developing measures to slow down and regress it based on this knowledge seem to be important tasks for medicine. Currently, disorders of the gut–liver axis have great importance in the pathogenesis of cirrhosis. However, gut dysbiosis, which manifests as increased proportions in the gut microbiota of Bacilli and Proteobacteria that are capable of bacterial translocation and a decreased proportion of Clostridia that strengthen the intestinal barrier, occurs even at the pre-cirrhotic stage of chronic liver disease. This leads to the development of bacterial translocation, a process by which those microbes enter the blood of the portal vein and then the liver tissue, where they activate Kupffer cells through Toll-like receptor 4. In response, the Kupffer cells produce profibrogenic cytokines, which activate hepatic stellate cells, stimulating their transformation into myofibroblasts that produce collagen and other elements of the extracellular matrix. Blocking bacterial translocation with antibiotics, probiotics, synbiotics, and other methods could slow down the progression of liver fibrosis. This was shown in a number of animal models but requires further verification in long-term randomized controlled trials with humans. Cirrhosis is the end result of chronic liver disease. It is a progressive fibrosis of the liver against the background of the death of its functioning tissue and perverted regeneration, which disrupt the liver’s structure and function. According to the latest data, there are 123 million people with cirrhosis worldwide . HoweverThe purpose of this review was to conduct a survey of published studies on the possible influence of gut microbiota pathology on liver fibrosis in chronic liver disease. Although many reviews have been published on gut–liver axis disorders in chronic liver disease, this is the first to specifically summarize the current data on the role of this axis in the development of liver fibrosis and discuss the possibility of preventing fibrosis by targeting it.Normally, most of the bacteria that enter the stomach with food and from the oral cavity die there under the influence of stomach acid, which makes the stomach the most sterile organ of the gastrointestinal tract . In the Ruminococcaceae and Lachnospiraceae families. As a rule, they are obligate anaerobes and cannot exist in the presence of oxygen; therefore, they do not penetrate oxygenated living human tissues. Many of them form beneficial short-chain fatty acids (SCFAs), which are used as a source of energy by enterocytes, strengthen the intestinal barrier, and carry out regulatory functions through specific receptors [Streptococcaceae and Enterococcaceae) that can survive and reproduce in oxygenated living human tissues [Firmicutes are represented by two main classes: Clostridia and Bacilli. Clostridia include most of the beneficial bacteria of the gut microbiota from the eceptors ,18,19,20eceptors . Among t tissues ,23,24; t tissues ,23,24. T tissues ,23,24.Bacterial translocation leads to the development of local and systemic inflammatory responses ,23,24,25Bifidobacteria, which are components of many probiotics and are considered beneficial [The role of Bacteroidetes in the gut microbiota is complex and controversial. Most of these bacteria are obligate anaerobes and are not capable of bacterial translocation. However, they have endotoxins, which have rather weak activity. The literature describes controversial effects of these bacteria ,27,28. Cneficial ,30.Enterobacteriaceae family, which are capable of bacterial translocation. Enterobacteriaceae endotoxins can penetrate a disordered intestinal barrier, stimulating the innate immune system and leading to the development of chronic inflammation in the intestinal wall and liver without living bacteria penetrating into the tissue  [Proteobacteria have active endotoxins and are represented in the intestinal microbiota mainly by facultative anaerobes of the ocation) ,32,33. TAkkermansia muciniphila, which is considered to be a beneficial bacterium that strengthens the intestinal barrier, suppresses the growth of harmful bacteria, and has an anti-inflammatory effect [Most representatives of the Verrucomicrobiota phylum in the human gut microbiota belong to y effect ,35,36. Iy effect ,35,36.Normally, the majority of gut bacteria are represented by beneficial Clostridia and neutral Bacteroidetes, while harmful Bacilli and Proteobacteria constitute a minority . In pathThere is also an important role in the process for small intestinal bacterial overgrowth (SIBO), which is defined as an increase in the content of bacteria in the small intestine of more than 100,000 cells per mL ,38,39,40Therefore, gut dysbiosis and SIBO, as pathologies of the gut microbiota, can potentially stimulate liver fibrosis through the activation of chronic liver inflammation in response to cellular and molecular bacterial translocation.Akkermansia in the gut microbiota decreases and the levels of harmful bacteria from the Bacillus class and the Proteobacteria phylum increase .The global prevalence of alcohol use disorder is 5.1% ,73. AlcoSimilar changes (growth of Bacilli and Proteobacteria and decreased abundance of Clostridia) are observed in HCV infection ,85,86, wPatients with MAFLD, which affects 25–50% of the world’s population , also haTherefore, even at the pre-cirrhotic stage, changes in the gut microbiota and intestinal barrier in patients with the most common chronic liver diseases can lead to the occurrence of bacterial translocation. However, almost always, these changes are less pronounced than in cirrhosis; that is, they increase as liver fibrosis progresses, completing a vicious circle.The development of liver fibrosis is based on the transdifferentiation of hepatic stellate cells (HSCs) into myofibroblasts, which intensively form collagen and other components of the extracellular matrix. The main activating signals for such transformation are transforming growth factor beta (TGF-β), platelet derived growth factor (PDGF), and damage-associated molecular patterns (DAMPs) from dead hepatocytes. The main source of these pro-fibrotic cytokines is Kupffer cells, which are activated by DAMPs and pathogen-associated molecular patterns (PAMPs). PAMPs include, among others, bacterial endotoxins (lipopolysaccharide (LPS)), peptidoglycan, and DNA. This activation occurs through Toll-like receptors (TLRs), among which the reaction of TLR4 with LPS should be especially noted. TLRs are relatively recently discovered receptors that respond to conserved PAMPs, such as LPS, bacterial flagellin, bacterial wall peptidoglycan, free RNA and DNA, and others. These receptors are important components of the innate immune system since they are responsible for its primary contact with pathogens and for launching the primary response to infection. TLR4 cannot bind directly, as the LPS molecule requires a complex assembly composed of CD14 co-receptors, which facilitates the transfer of LPS to the TLR4 complex and MD-2, an adapter molecule that modulates LPS recognition. Another cofactor is LPS-binding protein, which shuttles LPS to CD14 molecules. The association of these auxiliary molecules triggers the signal, resulting in the homodimerization of TLR4 molecules and consequent signaling. Therefore, the signals that stimulate liver fibrosis are the death of liver cells of any origin, which stimulates the activation and transformation of HSCs directly or by activating Kupffer cells, and the entry into the liver of bacterial translocation products  with the blood of the portal vein ,94. InteIn mice that had a deactivating mutation in the TLR4 gene, when the common bile duct was ligated, the degree of liver fibrosis was significantly less than in mice with normally functioning TLR4. Those mice also produced less TGF-β and collagen and had less hepatic macrophage infiltration in their livers. Similar results were obtained in chemical experimental models of simulated liver fibrosis with CCl4 and thioacetamide. Interestingly, the degree of hepatocyte damage did not differ significantly between normal and mutant mice, which provides evidence in favor of the two-hit theory  for the development of liver fibrosis. At the same time, mice with non-functioning TLR2 did not show significantly different levels of liver fibrosis after its development was stimulated compared to standard animals . Oral adIn experimental alcoholic liver disease, rats injected with antibodies against TLR4 had less severe liver fibrosis as well as lower levels of fibrosis biomarkers  than animals whose TLR4 activity was not suppressed . When LPKnockout of the CD14 gene (co-receptor of TLR4) in mice that underwent bile duct ligation led to less pronounced development of liver fibrosis and a smaller increase in the levels of fibrosis biomarkers, such as liver hydroxyproline and alpha-smooth muscle actin, compared with wild-type mice after this intervention. However, there was no significant difference between animal groups for biomarkers of liver damage, such as focal necrosis, biliary cell proliferation, and inflammatory cell influx .The non-absorbable antibiotic rifaximin in combination with lubiprostone, which enhances intestinal motility and, thereby, reduces SIBO, was found to diminish the severity of liver fibrosis in an experimental model of MAFLD . In anotLactobacillus rhamnosus GG reduced liver fibrosis after ligation of the common biliary duct in mice [11 colony-forming units of each of four bacteria: Lactobacillus paracasei, Lactobacillus plantarum, Leuconostoc mesenteroides, and Pediococcus pentosaceus) in mice treated for induced MAFLD with a high-fat, choline-deficient diet led to a decrease in Proteobacteria in the gut microbiota, which had been elevated on this diet, and endotoxemia, which correlated with the prevention of the development of severe liver fibrosis. Interestingly, a significant protective effect of this symbiotic against gut dysbiosis and endotoxemia developed only at week 18 of the experiment [Nigella sativa, showed the ability to attenuate the expression of CD14 and TRL4. This active biological substance significantly decreased the expression of small muscle actin alpha and collagen-I in the liver and liver fibrosis in LPS-challenged cell cultures with HSCs [Akkermansia muciniphila prevented a decrease in the protective function of the intestinal barrier; decreased the expression of smooth muscle actin alpha, PDGF, TGF-β, and collagen genes; and reduced the development of liver fibrosis in a complex toxic-metabolic model of liver damage [Probiotics based on  in mice . The useperiment . Thymoquith HSCs . The user damage .Therefore, the use of drugs targeting bacterial translocation shows promise in preventing and slowing the progression of liver fibrosis. However, we must determine the status of research on the effects of drugs targeting the gut microbiota and intestinal barrier to prevent the progression of fibrosis in people with chronic liver disease at the pre-cirrhotic stage. Such drugs approved for use in humans include probiotics, prebiotics, synbiotics, postbiotics, and antibiotics.Lactobacillus subtilis and Streptococcus faecium blocked the increase in blood LPS levels in alcoholic hepatitis but only in the cirrhosis subgroup. The number of Escherichia coli decreased in the feces of the probiotic groups. Changes in the levels of other biomarkers were not compared between the probiotic and placebo groups in this randomized controlled trial. Liver fibrosis was not assessed in this study [Lactobacillus rhamnosus R0011 and Lactobacillus helveticus R0052 increased the abundance of beneficial Faecalibacterium prausnitzii and decreased the amount of harmful Escherichia coli and serum LPS levels in alcoholic hepatitis, among other changes. Liver fibrosis was not assessed in this randomized controlled trial [Short-term use (7 days) of probiotics based on is study . Short-ted trial . Other sed trial ,109.The state of the intestinal barrier and the gut microbiota, and the level of endotoxemia and liver fibrosis were not assessed when studying the effects of probiotics in chronic viral hepatitis C . ProbiotLactobacilli, Bifidobacteria, and Streptococcus thermophilus for 12 weeks led to decreased liver stiffness and Enterobacteriaceae and Bacilli levels in the gut microbiota. Other biomarkers of gut–liver axis status were not examined in this study [Bifidobacterium, Lactobacillus, Lactococcus, and Propionibacterium, had no significant effect on liver stiffness after 8 weeks of use [Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus lactis, Bifidobacterium bifidum, Bifidobacterium infantis, and Bifidobacterium longum did not have a significant effect on liver stiffness and the expression of tight junction proteins in the epithelium of the small intestine villi [A systematic review was recently published on the effects of probiotics, prebiotics, and synbiotics in MAFLD . The useis study . In conts of use . The uses of use . Six-monne villi . Indicatne villi . None ofne villi .The use of the prebiotic oligofructose for 36 weeks did not significantly affect the levels of endotoxemia and liver fibrosis in metabolic-associated steatohepatitis . Liver fThe prebiotic inulin, taken for 17 days, did not have a significant effect on markers of bacterial translocation in alcoholic liver disease . There hBifidobacterium animalis did not show a significant decrease in stiffness [Lactobacillus casei, Lactobacillus rhamnosus, Streptococcus thermophilus, Bifidobacterium breve, Lactobacillus acidophilus, Bifidobacterium longum, Lactobacillus bulgaricus, and fructo-oligosaccharide, taken for 28 weeks, significantly reduced liver stiffness in this disease [Patients with MAFLD who received synbiotic treatment consisting of fructo-oligosaccharides and tiffness . In cont disease . Other s disease ,125,126.The effects of synbiotics on bacterial translocation and liver fibrosis in alcoholic liver disease and chronic viral hepatitis B and C at the pre-cirrhotic stage have not been studied in humans. The effects of postbiotics and the long-term use of antibiotics on bacterial translocation and liver fibrosis have not been assessed in chronic liver diseases at the pre-cirrhotic stage in human studies.In contrast to the experimental models, human studies, in most cases, have not shown a positive effect of therapy targeting the gut microbiota in terms of regression of liver fibrosis in chronic liver disease at the pre-cirrhotic stage. This contradiction may be due to the fact that the natural course of most chronic human liver diseases is very long, and liver fibrosis develops several years or decades after the onset of pathogenic action. It should also be remembered that the life expectancy of experimental animals is significantly lower, and they have higher metabolic rates, which makes it possible to demonstrate the effect of various long-acting drugs. Therefore, drugs for the prevention of liver fibrosis should be taken for years, which means that clinical studies of their effectiveness should last several years. The short-term use of these drugs, as is typically practiced in clinical trials, is clearly insufficient to demonstrate a positive effect in most cases. The use of long-term probiotics in MAFLD has shown a positive effect on liver density (an indirect marker of liver fibrosis) in a meta-analysis of human studies. Therefore, the hypothesis that therapy should be aimed at preventing the development of bacterial translocation in chronic liver disease at the pre-cirrhotic stage cannot be rejected. New studies on the long-term use of drugs that affect this link in the pathogenesis of liver fibrosis in such patients are needed for final verification of this hypothesis.In chronic liver diseases, gut dysbiosis occurs even during the pre-cirrhotic stage, manifesting as an increase in the proportion of harmful facultative anaerobes capable of bacterial translocation (Bacillus class and Proteobacteria phylum) and a decrease in the proportion of beneficial obligate anaerobes (Clostridia class), which strengthen the intestinal barrier, in the gut microbiota. In addition, there is disorder of the intestinal barrier and, quite often, SIBO in these diseases. All of this leads to the occurrence of bacterial translocation, one of the main manifestations of which is endotoxemia. Organisms undergoing bacterial translocation with the blood of the portal vein enter the liver tissue, where they activate Kupffer cells through TLR4. The latter, in response to activation, produce many cytokines, including profibrogenic ones, which activate HSCs and stimulate their transformation into myofibroblasts that produce collagen and other elements of the extracellular matrix . Blockin