Aequiyoldia eightsii in South America and Antarctica is complicated by mitochondrial heteroplasmy and amplification bias in molecular barcoding. In this study, we compare different data sources  sequences; nuclear and mitochondrial SNPs). Whilst all the data suggest that populations on either side of the Drake Passage belong to different species, the picture is less clear within Antarctic populations, which harbor three distinct mitochondrial lineages (p-dist ≈ 6%) that coexist in populations and in a subset of individuals with heteroplasmy. Standard barcoding procedures lead to amplification bias favoring either haplotype unpredictably and thus overestimate the species richness with high confidence. However, nuclear SNPs show no differentiation akin to the trans-Drake comparison, suggesting that the Antarctic populations represent a single species. Their distinct haplotypes likely evolved during periods of temporary allopatry, whereas recombination eroded similar differentiation patterns in the nuclear genome after secondary contact. Our study highlights the importance of using multiple data sources and careful quality control measures to avoid bias and increase the accuracy of molecular species delimitation. We recommend an active search for mitochondrial heteroplasmy and haplotype-specific primers for amplification in DNA-barcoding studies.The species delimitation of the marine bivalve species complex  COI) is one of the “gold standards” in the molecular barcoding of animals [The accurate delimitation and identification of species is pivotal to understanding the evolution of biodiversity and the response of communities to environmental change. DNA barcoding is a commonly used approach for assigning individuals to a known species based on standard reference DNA sequences (species identification) and for detecting still unknown species through deviations from known sequences (species delimitation). Ever since DNA barcoding was established , the seq animals ,4. The u animals .Despite the widespread application of this convenient approach  and theHeteroplasmy has been found in a wide range of taxa ranging from mollusks and arthropods ,11,12 toA. eightsii (previously of the genus Yoldia) is widely distributed in the Southern Ocean, inhabiting the soft-substratum ecosystems of South America, the Antarctic Peninsula, and several Subantarctic islands. This wide distribution range on both sides of the Drake Passage has given rise to several revisions of its taxonomic status [COI p-distance), a third linage in South America (6.5–7.5% COI and 1.2% ITS p-distances) with respect to the Antarctic peninsula, and two additional mitochondrial lineages on Kerguelen Island and the Falkland/Malvinas Islands. However, the scarcity of available nuclear information has hampered formal taxonomic delimitations thus far; hence, A. eightsii is still considered a single species.DUI is widespread within bivalve subclasses, including among Protobranchia , the mosc status . Recentlc status  highlighc status , have dic status  suggesteThe advances in speed and accuracy in tandem with reduced costs of next-generation sequencing (NGS) technologies have facilitated the generation of population-scale genomic surveys. Transcriptome sequencing (RNA-seq) is particularly useful for studying non-model organisms lacking reference genomes . RecentlA. eightsii populations. Our analysis compares the results of a classic barcoding approach based on selected mitochondrial and nuclear gene fragments with SNPs derived from the deep sequencing of expressed genes, with a particular focus on how instances of heteroplasmy, if any, may affect population genetic and phylogenetic inferences.Based on the hypothesis of heteroplasmy being a potential confounding factor in population genetics and phylogenetic analyses, this study evaluates patterns of genetic divergence between South American and Antarctic A. eightsii samples were collected in South America (Magellanic region) and Antarctic Peninsula in the austral summer season of October 2017 until February 2018. Bivalves from the Magellanic region (n = 100) were collected by SCUBA divers in the shallow subtidal zone near Punta Arenas  on a single day in October 2017. Antarctic bivalves (n = 104) were collected between January and February 2018 from three sites in Potter Cove (PC), an 8 km2 glacial fjord on King George Island, South Shetlands , in shallow waters between 6 and 20 m using a Van Veen grab. The maximum distance between the three collection sites in PC was 1 km, and the sites were sampled within approximately one week. An additional subset of samples (n = 34) collected in Hangar Cove in February 2007  and Potter Cove in January 2016 and conserved at −20 °C was provided by colleagues from the British Antarctic Survey and the University of Cordoba (Argentina), respectively. These samples were exclusively used for the mitochondrial and nuclear loci analyses. In total, we obtained 238 individual organisms.During each sampling period, bivalves were transported to the local research facility in insulated containers containing seawater and sediment from the sampling site and were dissected under a stereomicroscope after recording their individual weights, shell lengths, and widths. For each individual, three tissue types were conserved: foot and mantle tissue samples were conserved separately in RNAlater  and stored at −80 °C, and the rest of the soft body tissue was stored in Ethanol 95% at 4 °C until further analysis. In total, 168 bivalves were included in the analysis. All of them were utilized for the mitochondrial and nuclear marker analyses, and a subset of 70 individuals were analyzed to obtain SNPs via RNA-seq data.COI) amplified using Folmer primers and haplotype-specific primers (see histone H3), and one nuclear ribosomal gene . Total genomic DNA was extracted from muscle tissue with QIAamp DNA Mini Kit  following the manufacturer’s protocol (regarding DNA purification from tissues). COI amplification and sequencing rwas performed for 155 individuals ; histone H3 and 18S fragments were amplified and sequenced for 72 animals . DNA purity and concentration were assessed using a Nanodrop Spectrophotometer® ND-1000 . Purified genomic DNA was diluted to 10 ng/μL and used as template for Polymerase Chain Reaction (PCR) amplification. All PCRs were carried out in 25 μL reaction volumes comprising 5.0 μL 5X Colorless GoTaq® Flexi Buffer , 2 μL of MgCl2 (25 mM), 2.5 μL of dNTP mix (2 mM), 0.25 μL of Betain (5M), 0.125 of each forward and reverse primer (100 μM), 0.15 μL of GoTaq® G2 Flexi DNA Polymerase , and 3 μL of DNA extract (10 ng/μL). A fragment of 640 bp corresponding to COI was amplified using Folmer primers [histone H3 and 18S fragments , new primers were designed based on sequences of species from the same family published in [histone H3: forward primer: 5′-GAA AAT CTA CCG GTG GCA AG-3′, reverse primer: 5′-GTG TCC TCG AAC AAA CCA AC-3′; 18S: forward primer: 5′-AAG TAC AGA CTC TCA GTA CGG-3′, reverse primer: 5′-GAA GGC CAA CAA AAT AGA ACC-3′) and the PCR program used for both fragments consisted of the following steps: 2 min at 95 °C, 35 cycles at 95 °C for 20 s, 56 °C for 20 s, 72 °C for 40 s, and a final step of 8 min at 72 °C. After the amplification, an initial quality assessment of the PCR was conducted by placing the product on 2% agarose gel. All PCR products were sequenced for both strands by Eurofins Genomics GmbH  using the same primers that were utilized during amplification. COI, histone H3, and 18S sequences were aligned using the ClustalW algorithm of CodonCode Aligner program , and all chromatograms were inspected visually for sequencing mistakes. For COI, a haplotype network was constructed using the Neighbor-Joining algorithm and implementing the Hasegawa–Kishino–Yano (HKY) model of substitution in Geneious using default parameters  and Haplotype Viewer ).The molecular markers included one mitochondrial gene A subset of 70 individual samples out of the total included in mitochondrial and nuclear loci analysis were analyzed to obtain SNPs via RNA-seq data. A total of 70 transcriptomic libraries  were created in this study for animals from Punta Arenas and Potter Cove . Libraries were prepared using the Illumina TruSeq® Stranded mRNA Sample Preparation Kit starting from 1 µg of total RNA. The Poly-A-containing mRNA molecules were purified using poly-T oligo-attached magnetic beads. Subsequently, the mRNA was fragmented using divalent cations under elevated temperature and copied into the first strand of cDNA using reverse transcriptase and random primers. This process was followed by second-strand cDNA synthesis using DNA Polymerase I, RNase H, and dUTP instead of dTTP to achieve strand specificity and remove the RNA template. Following adapter ligation, products were enriched by PCR and purified to create a cDNA library. Libraries were validated and quantified using a LabChip® GX Touch . All the samples were pooled and cleaned using magnetic beads to remove the remaining primer content. Final cDNA concentration was measured using LabChip® GX Touch . The pool of samples was sequenced at the Alfred Wegener Institute on an Illumina NextSeq 500 sequencer using the NextSeq High Output Kit v2 (150 cycles) with a paired-end protocol.Samples of mantle tissue (5–30 mg) were homogenized in Trizol reagent  using a Precellys homogenizer . Total RNA was isolated from each sample using the Direct-zolTM RNA MiniPrep Kit  according to the manufacturer’s instructions. The concentration and quality of the RNA were determined using a Nanodrop Spectrophotometer ND-1000  and LabChipRaw reads were quality-controlled by FastQC v. 0.11.7 . Adapter sequences were removed using bbduk.sh from the BBtools suite version 36.38)  with the.38  withn = 1000) the sample matrices as described in Excoffier et al. [For the SNP analysis, quality-filtered paired-end reads were aligned to the de novo transcriptome using bowtie2 v2.3.4.1 . Alignmer et al. . All ther et al.  using thr et al. , adegener et al. , poppr vr et al. , ade4 [4r et al. , and pegr et al. .COI data; see COI sequence amplified with Folmer primers in order to track the haplotype identification based on COI. A subset of samples of the four haplotypes from every location  were amplified with both primer combinations. PCR conditions for both primer combinations were the same as for COI with Folmer primers, with the exception of the annealing temperature (56 °C). The size of PCR products was determined on 2% agarose gel, and a representative subset of samples of the mitochondrial haplotypes successfully amplified for one or two variants (n = 32) was sequenced on both strands by Eurofins Genomics GmbH . Finally, samples were aligned using the ClustalW algorithm of CodonCode Aligner program . This analysis allowed us to group the animals carrying a single or two combined haplotypes in different genetic clusters (mitotypes), which was further considered for SNPs.Observations of raw SNP data , however, ambiguous base calls that suggested the possibility of the co-existence of several COI alleles in the template  were observed.There were no differences between the data see . In mostCOI sequencing with the Folmer primers show a clear differentiation between Antarctic and South American individuals (h4) but also a division between two major Antarctic clades  are labeled in COI sequences of any haplotype or location.The results of the The co-existence of two haplotypes in a single individual was detected in several Antarctic individuals from PC (19 out of 47) and HC (1 out of 16)  but not in South American organisms (0 out of 16) (h4). In all but one individual that had been identified as carrying the haplotype “h3-only” with Folmer primers see , the hapThe Illumina sequencing resulted in 454.8 million reads (average reads per library: 6.5 million), of which 388.4 million reads were used for the de novo transcriptome assembly after being filtered for quality. A total of 389,929 transcripts were assembled, with an average length of 764 bp. The BUSCO assessment of the completeness of the transcriptome showed very high quality, for which 98.7%  of the orthologs of the public database “metazoa_odb10” were present in the assembly.The results of the Principal Component Analysis (PCA) for nuclear and mitochondrial SNP data are shown in p < 0.001), with most of the variation occurring between locations  and a minor proportion occurring between mitotypes within locations (7.8%) and among individuals (2.6%). The AMOVA performed on the nuclear SNPs presented significant differences between locations (67.7%) and among individuals (31.7%) but not between mitotypes within locations. This information is summarized in The AMOVA results confirmed the clear patterns observed in the PCAs. For mitochondrial SNPs, all three levels of variance partitioning were highly significant  showed a high density of highly differentiated SNPs  between animals from the two continents a,b, thusCOI). We present a case study in a non-model marine bivalve, in which the standard barcoding approach using Folmer primers is systematically biased by the occurrence of several distinct mitochondrial genomes in the population, including heteroplasmy, which, in the absence of independent data , would lead to an erroneous interpretation of the number of species and their distribution patterns  is close to our sampling location (Potter Cove), it is reasonable to assume that all our Southern Ocean samples belong to A. eightsii  sensu strictu, whilst our South American samples (haplotype h4) belong to an as yet undescribed species that has been confused with it. However, a full taxonomical revision including the formal description of the new species of Aequiyoldia is beyond the scope of this paper.Both mitochondrial and nuclear SNP analysis showed significant differences between Antarctic and South American bivalves, thus confirming the clear pattern observed in the PCAs and in the GST’ estimations. The bimodal distribution of pairwise differences in both data sources demonstrate that South American and Antarctic populations are genetically much more similar within the limits of either continental waters but highly distinct when compared between them. When viewed in conjunction with the marked differences in shell size see , the mosWhilst the extrapolation from one mitochondrial marker gene to the assumed congruent differentiation of the nuclear genome that the standard barcoding procedure implies seems justified in the comparison across the Drake Passage , the samA. eightsii are strongly dependent on the source of data used. Although a differentiation pattern of a magnitude similar to that of the species-level trans-Drake comparison is also present within the Southern Ocean in the mitochondrial COI sequence barcoding data  and the mitochondrial SNPs  suggest a mutually incompatible differentiation pattern. The COI barcode sequences suggest a structure corresponding to (h1 + h2) vs. h3, whereas for the mitochondrial SNPs the most apparent division is orthogonal to the COI sequence data, namely, (h1h1 + h1h3) vs. (h2h2 + h2h3), possibly h3h3 by itself; thus, individuals are most strongly characterized by possessing either h1 or h2 with varying proportions of h3 admixed with either of them. This finding is not only different but incongruent and cannot easily be reconciled by assuming a different degree of taxonomic resolution in the various data sources.In the Southern Ocean, the most notable patterns of genetic differentiation in ing data c for allCOI gene turned out to be a good first indicator of major genetic diversity in the trans-Drake comparison. Instead, the heteroplasmic mitochondrial condition observed in some Antarctic specimens significantly interfered with its taxonomic classification when addressed by traditional molecular approaches. Considering the magnitude of the differences found by González-Wevar et al. [COI, it seems likely that they, too, had detected the division between h3 and (h1 + h2) and hence suggested that these two lineages were separate species, a conclusion that is not supported by our more comprehensive dataset. Our mitochondrial and nuclear SNP dataset and the coexistence of haplotype h3 with either haplotype h1 or h2 in some organisms exclude cryptic speciation between these two groups as an explanation for the observed patterns.The classical molecular barcoding approach based on the amplification and sequencing of a fragment of the mitochondrial r et al.  based onA. eightsii serves as a model case demonstrating that if heteroplasmy occurs, it can lead to false taxonomic inferences with an inflated degree of confidence. Correct taxonomic delimitation and identification is essential for the monitoring and conservation of biodiversity, especially in areas of the globe particularly threatened by climate change such as Antarctica.The widespread occurrence of mitochondrial heteroplasmy linked to Doubly Uniparental Inheritance (DUI) in bivalves and the evidence of mitochondrial heteroplasmy in a wide variety of taxa from crustaceans to vertebrates ,15 highlCOI to the entire (nuclear) genome, the apparent division of COI sequences into three almost equidistant groups, namely, (h1 + h2), h3, and h4, is likely the result of a separate evolutionary history of the three in isolation from one another. The mitotype h4 is separated by the Drake Passage and the Polar Front from its nearest relatives in the Southern Ocean, a condition that remains so until today [Aequiyoldia clades characterized by major haplotype groups in Antarctica likely happened in the Pliocene; however, unlike their South American relatives, all three of them occur at least in partial sympatry today. The most likely scenario for the divergent patterns found in the mitochondrial and nuclear data is that the differentiated mitochondrial lineages persisted even after the groups made secondary contact, which is a consequence of their independent, non-recombining, clonal inheritance. On the other hand, the pattern of differentiation in the nuclear genome eroded away over time as a result of the recombination of the hybridizing lineages, possibly comprising a case of speciation reversal as suggested for other Antarctic invertebrates [Although our data strongly question the validity of the extrapolation from a fragment of il today . Howeveril today , thus intebrates . Severaltebrates ,54, polytebrates , and echtebrates , among oCOI fragment to the entire genome as implied in molecular barcoding may not be justified when mitochondrial heteroplasmy is involved. In our study, an amplification bias with Folmer primers in the presence of two competing templates available for amplification in a heteroplasmic scenario resulted in the amplification of either of the two existing variants (Population genetics, molecular barcoding, and molecular systematics critically depend on extrapolations made from small subsets to larger scales. Our results show that the confidence in the correctness of the extrapolation from a variants , therebyCountermeasures against this shortcoming include an active search for double peaks in chromatograms in the Sanger sequencing results, the use of haplotype-specific primers whenever there is evidence of sequence competition, and the use of alternative sequencing methods in combination with an active search for segregating sites.


The limitations of significant tool wear and tool breakage of commercially available fluted micro-end mill tools often lead to ineffective and inefficient manufacturing, while surface quality and geometric dimensions remain unacceptably poor. This is especially true for machining of difficult-to-machine (DTM) materials, such as super alloys and ceramics. Such conventional fluted micro-tool designs are generally down scaled from the macro-milling tool designs. However, simply scaling such designs from the macro to micro domain leads to inherent design flaws, such as poor tool rigidity, poor tool strength and weak cutting edges, ultimately ending in tool failure. Therefore, in this article a design process is first established to determine optimal micro-end mill tool designs for machining some typical DTM materials commonly used in manufacturing orthopaedic implants and micro-feature moulds. The design process focuses on achieving robust stiffness and mechanical strength to reduce tool wear, avoid tool chipping and tool breakage in order to efficiently machine very hard materials. Then, static stress and deflection finite element analysis (FEA) is carried out to identify stiffness and rigidity of the tool design in relation to the maximum deformations, as well as the Von Mises stress distribution at the cutting edge of the designed tools. Following analysis and further optimisation of the FEA results, a verified optimum tool design is established for micro-milling DTM materials. An experimental study is then carried out to compare the optimum tool design to commercial tools, in regards to cutting forces, tool wear and surface quality. Micro-milling is one of the most cost effective and flexible manufacturing processes to produce micro-components, micro-features and micro-structured surfaces as it provides relatively high material removal rates and allows for manufacturing of complex three-dimensional surfaces, while maintaining high precision and component accuracy. One of the earliest applications for the micro-milling process was in micro-structure and micro-component fabrication, which included blades of an impeller or turbine, walls of a microchannel, microcolumns, and fins of a heat exchanger. Presently, these microstructures have been widely applied in micro-fuel cells , microflThe issues of poor rigidity, weak cross sections and geometries, fragile cutting lengths and overall poor mechanical strength all arise from the fact that current commercially available micro-milling tools are generally downscaled from macro-milling tools .The probIn order to combat the inherent issue of tool wear and poor surface quality during micro-milling of DTM materials, a fundamental investigation of the optimal tool geometry is presented in this study. Beginning with a design approach to micro-milling tool geometries, a novel tool design is established that focuses on alleviating the known issues of current tool geometries that lead to significant tool wear and tool breakage during machining of very hard materials. From the developed design, static stress and deflection finite element analysis (FEA) is carried out to identify stiffness and the maximum deflections as result of distributed applied loads, analogous to cutting forces during micro-milling. A fractional parametric experimental study was then carried out on the fabricated tools to compare the developed tool design with commercially available fluted tools, in regards to cutting forces, tool wear and surface quality. Finally, a tool wear criterion was developed to characterise the condition of micro-milling tools.The reduced stiffness of commercial fluted tools often leads to large tool deflection, leading to high cutting forces and uneven chip loading, eventually resulting in tool chipping and tool breakage as well as significant tool wear. It was determined by Fang et al.  through (i)High stiffness due to a large cross-sectional area along the cutting-edge length, with optimised tool features and angles including large taper angle neck, short cutting-edge length, short overall length, as well as optimised radial and axial, rake, and clearance angles.(ii)High strength through increased material volume around the cutting edge radius and peripheral cutting edges, optimised tool edge radius and peripheral cutting edge radius, increased bulk cross section and reduced depth of cutting channels.(iii)High durability due to the specification of cemented carbide tool material, such as fine grain WC.(iv)A simplified, balanced and radially symmetrical geometry design with two straight cutting edges with dodecagon clearance faces to ensure that fabrication of the tool is both efficient and cost-effective.(v)Efficient chip evacuation through optimised channel and cutting edge geometry with the addition of a chip breaker on the rake and flank face of the tool to prevent pinching and to direct chip flow radially inward to ensure small, discontinuous chip formation.For micro-milling, appropriate geometrical design of the tool is essential for achieving robust tool stiffness and mechanical strength, to prolong tool life and ensure effective machining. Specifically, the geometry of micro-end mill tools should be designed depending on the type of applications, in this case, for machining of very hard and wear resistant materials. Therefore, this design specification outlines several key areas for improving overall tool stiffness, strength, durability, ease of fabrication and ensuring efficient chip formation and evacuation, while minimising contact between the tool and the workpiece. Therefore, the proposed tool is a micro double straight edge end-mill (DSEE) with the following criteria.The DSEE tools were designed using Autocad for tool diameters 800 µm, 600 µm, 400 µm and 200 µm. FEA was then conducted using Abaqus to study the linear, steady-state, static analysis of the distribution of the maximum principal stress and deflection of the cutting length under applied loads during micro-milling. The performances of both tools were investigated in relation to tool geometry, and the results from which led to further optimisation and then fabrication of the DSEE tool design.Figure The main objective of this FEA analysis is to evaluate the deflection and Von Mises stress distribution at the tool tip of the DSEE designed tool and compare the results with a typical, commercially available 2-flute micro-milling tool. The results led to further optimisation of the DSEE tool design, from which tools were then fabricated and a full experimental investigation was conducted.X direction, as can be seen in Fig. During the micro-milling process, the load condition on the cutting edge is complex and is a result of the infeed, crossfeed and thrust forces acting on the tool, which are directly related to the uncut chip thickness (UCT) , which iThe model definition is an Abaqus explicit 3D static stress/displacement analysis with 1 second step increment. Second-order elements provide higher accuracy in Abaqus/Standard than first-order elements for “smooth” problems that do not involve severe element distortions . They capture stress concentrations more effectively and are better for modelling geometric features, i.e., they can model a curved surface with fewer elements. Finally, second-order elements are very effective in bending-dominated problems . Hence, Two types of tools were simulated using this model and the results of both the average and maximum deflection and Von Mises stress distributions were compared against each other. The designed DSEE tool was against a typical helical 2-flute tool design, for a range of tool diameters, namely 800 µm, 600 µm, 400 µm and 200 µm. The first set of simulations compares the DSEE against the 2-flute tools due to a distributed load of 3 N at the cutting edge. For the second set of simulations, we again compare the DSEE and 2-flute tools in relation to deflection and Von Mises stress, this time with a scaled distributed load with reduction in tool cutting diameter, simulating the differences of cutting force applied to each tool diameter during real application. Namely, 800 µm, 600 µm, 400 µm and 200 µm tools with 4 N, 3 N, 2 N, and 1 N distributed load, respectively. The material along the full length of the tools was WC, and the material properties were determined from Refs. , 27, as The results of each simulation are visualised using contour plots for deflection and Von Mises stress, taking the maximum values for the comparison between the designed tool and the common commercially available 2-flute design. The field output deflection gives an indication on the relative rigidity and stiffness of the tools, while Von Mises stress distribution identifies if the material at the cutting edge will yield or fracture under the applied distributed load. To more effectively evaluate the tool with real application, the results of the Von Mises stress are compared with the transverse rupture strength (TRS) of WC. TRS is a material property defined as the stress in a material before it yields, and therefore can be used to compare against the Von Mises principal stress to determine if tool breakage will occur during the simulation. The TRS for WC tools throughout this work is considered to be 3.2 GPa. An example of the contour plot of deflection is shown in Fig. Figure The results of the second set of simulation results, i.e., investigation of scaled cutting forces, are presented in Figs. With the results of the FEA simulation, the DSEE tools were then fabricated for diameters 800 µm and 600 µm from fine grain WC. Images of both the designed DSEE tool and conventional 2-flute tools can be seen in Fig. Sa) prior to experiments. Tool materials for both the developed tool and high quality commercial tool were high grade WC with very similar dimensions. Both were fabricated by Louis Belet Swiss Cutting Tools and were uncoated. The control 2-flute tool was “Micro end mill Z2”. The parameters for each experiment were depth of cut, which was 50 µm and 35 µm for the 800 µm and 600 µm diameter tools, and length of cut 2 mm and 1.5 mm, respectively. The experiments were full immersion up slot micro-milling, ran under dry machining conditions. A Taguchi L36 [The examined process outputs during experiments were cutting force, surface quality, in regard to surface roughness and burr formation, chip size and tool wear. The process input parameters were tool design, workpiece material, spindle speed and feed rate. The workpiece materials were ISO 5832-4 cobalt-chromium-molybdenum (CoCrMo) alloy and ISO 5832-3 titanium (Ti6Al4V) alloy, and were precision ground flat and to surface roughness of around 100 nm in surface roughness  in Eq. (AW) give a value for the area of tool wear which occurred, as seen in Tables 2 and 300 µm2 for 800 µm and 600 µm diameter tools, respectively. Therefore this area was removed from calculation of flank wear area.ACE is cutting edge area.To characterise tool wear more accurately, a tool wear criterion was developed. This criterion characterised each tool regarding type of tool wear occurring, on both flutes/edges, how much wear occurred, the overall condition of the tool, a recommendation on when to change the tool, and finally information on tool runout. The criterion considers two methods for the determination of tool condition. The first is average tool wear between both flutes on the rake and flank faces, while the second is reduction in tool diameter, to fully encapsulate the state of the tool. For the first method, a tool wear formula was developed from the analysis of the results of cutting force and surface roughness, to determine how much wear occurred on each face and each flute. The formulae for both are below. The tool condition parameter  in Eq.  was deteDr) uses different values for TP and is the original diameter (Do) multiplied by TP, as shown in Eq.  ≈ 4.3 kHz, fn (y) ≈ 4.6 kHz and fn (z) ≈ 4.4 kHz. A sampling rate of 25 kHz was used which was far higher than the natural frequency of the dynamometer. The dynamometer was aligned such that the crossfeed cutting force was in the Fx direction, infeed cutting force in the Fy direction and thrust force in the Fz direction. The workpiece materials, in wrought cylindrical bar form, with dimensions of 30 mm diameter and 20 mm height, was clamped into a holding fixture which in turn was bolted to the dynamometer. The dynamometer was connected to a Kistler Lab Amp Type 5167AX1, which was both a charge amplifier for multi-component force measurements, as well as a data acquisition device. The signal data were then analysed and processed on a host computer using the Kistler DynoWare software.Experiments were conducted on an ultra-precision 4 axis machining centre with CNC in a temperature-controlled lab at (20 ± 1) °C. A Kistler dynamometer Type 9119AA2 was mounted on a backing plate and mounted on the work holding spindle of the machine to measure three component cutting forces, Sa at the start, end and over the entire machined surface of the slot. Burr formation was qualitatively analysed and characterised optically, using the Keyence microscope at ×500 magnification at key areas along the slot. Finally, cutting chips were collected and observed under the microscope at high magnification of ×2 000 to determine chip size and shape. Ten measurements of both length and width dimensions were taken for each chip, from which the average chip dimension was plotted with the standard deviation.Each of the experiment blocks was prescribed a new tool , and measured after each experiment for analysis of tool wear on a Keyence VHX-5000 optical microscope. Measurement of tool wear involved identifying modes of tool wear that occurred, as seen in Fig. Fx) cutting force, in blue, is lower for DSEE tools, while infeed (Fy) in red, is similar for both tool types. However, thrust force (Fz) in pink, is significantly lower for DSEE tool in comparison to 2-flute tool and these results are consistent over both tool diameters. It also must be noted that cutting force was affected by significant tool wear that occurred during 2-flute 800 µm Exps 3 and 6, while complete tool breakage occurred during Exps 8 and 9. The results for mean cutting force for the Ti6Al4V workpiece material are shown in Fig. Results of mean cutting forces in the three component directions throughout each experiment for the CoCrMo workpiece material for both diameters and tool types are displayed in Fig. Typical cutting force data for equivalent experiments comparing the DSEE and 2-flute 800 µm tools are presented in Figs. Sa analysis are presented below in Fig. Sa (purple) values were measured across all DSEE 800 µm experiments for CoCrMo. Considering each tool was used for 3 experiments, i.e., tool 1 for Exps 1–3, tool 2 for Exps 4–6, etc., only minor increases in surface roughness were measured after each tool use. This indicates that overall, only very little tool wear occurred and the tool remained robust without edge chipping throughout each experiment block. Surface roughness increases at start (blue) through Exps 1–3, Exps 4–6 and Exps 7–9, as less stable machining occurs at the start of the slot where cutting force ramps up. However, lower surface roughness at the end of the slots (yellow) than at start indicates very minor tool wear reduced the tool edge radius, which is known to help improve surface roughness values [Sa values occurring in Exp 5 for both tool sizes, namely 25 000 r/min spindle speed and 20 mm/min feed rate for the DSEE 800 µm, and 35 000 r/min spindle speed and 28 mm/min feed rate for the DSEE 600 µm tool. Increasing feed rate within each spindle speed block only slightly increased surface roughness, as did increasing spindle speed through each block. Overall, micro-milling experiments with the DSEE tools produced very low surface roughness values, with Sa < 0.1 µm across all experiments on the CoCrMo workpiece. The results from the Ti6Al4V workpiece material follow the same trends for both DSEE tool diameters, as seen in Figs. Sa between 0.2 µm and 0.4 µm across all experiments. One reason for this may be due to dry machining of Ti6Al4V material produces excessive friction and high cutting temperatures, leading to poor chip formation and ploughing mode of material removal, which is in line with the results presented on burr formation in the next section below. Overall, the results still indicate very little tool wear occurred for both diameter tools with relatively low surface roughness.The results of s values . The othSa > 0.2 µm in most experiments and up to Sa > 1 µm in some cases. Overall trends in this data are difficult to determine as a result of the excessive tool wear occurring inside each experiment block for both tool diameters. However, this was not the case during 2-flute experiments on the Ti6Al4V workpiece, as seen in Figs. In contrast, very high overall surface roughness values were measured for 2-flute tools. This was especially true for CoCrMo workpiece material, where extremely high surface roughness values were measured for some experiments. The reason for this was that the 2-flute tools suffered major tool wear and complete tool failure during experiments on the CoCrMo workpiece, as can be seen in Figs. Overall, DSEE tools generally showed straight walls with little burr formation, while 2-flute tools generally showed rougher walls with large burr formation. Examples of the qualitative analysis on the machined slots are presented in Fig. The results of Ti6Al4V material are much poorer for both tool types and diameters. Larger and more regular burrs formed on the side wall in the same locations for each experiment, as can be seen in Fig. The results of chip size and shape analysis are presented in Figs. Overall, the results indicated that shorter, more discontinuous chips were formed during machining by the DSEE tools, leading to better tool performance and lower cutting forces during machining, as verified by the cutting force results in Sect. 2 and 422.5 µm2. This demonstrates that this set of experiments, Block 3, provided the worse process outputs and that the machining parameters were too high. Tool 2 had the lowest rake and flank face tool wear, with no significant runout or chipping occurring. Therefore, Block 2 of experiments provided the most optimal machining conditions. Tool 1 had overall low tool wear again, although chipping was identified on flank face 1. For Method 2, all three tools could be still be considered “New”, as no significant diameter reduction occurred and slot geometrical integrity was maintained, although Tool 3 was very close to be considered “Minor” wear. Conversely, the results for 2-flute 800 µm tool show “Complete Failure” occurred for each face of each flute in relation to Method 1 analysis. Tool 3 suffered the most tool wear for both flutes, and for both rake and flank faces, while Tool 2 offered the relatively lowest wear, agreeing with results that Block 2 had the most optimal machining parameters for CoCrMo. Analysis of Method 2 result shows only minor and major reduction of diameter occurred for Tools 1 and 2, respectively. Even though the rake and flank face cutting edges were completely destroyed, the outer diameter was still maintained. Therefore it is important to evaluate the tool wear in regards to both methods to better evaluate the overall condition of each tool.The results of the tool wear analysis using the developed tool wear criterion are presented in Fig. Comparing the results for both 600 µm tools for CoCrMo workpiece material in Fig. Figure Figure (i)The DSEE tool shows overall much lower cutting forces than the 2-flute tools for the two types of workpiece materials. Crossfeed cutting force is slightly higher for DSEE tools than for 2-flute tools while thrust force is far lower, providing more stable machining conditions and better surface quality. The cutting force also increases with feed rate, per block of spindle speeds, and that higher spindle speed blocks with higher feed rates also result in higher cutting force.(ii)Sa values occurring for feed per tooth of 0.4 µm for both tool diameters. Slightly lower surface roughness at the end of the slots show that very minor tool wear reduced the tool edge radius and removed sharp edges of the DSEE tool, thereby improving surface roughness values at the end of the slot. However, major tool chipping and tool breakage throughout 2-flute experiments resulted in very high surface roughness values. Little variation in surface roughness over the entire slots machined by DSEE tools indicate that the tool remained in good condition without edge chipping throughout each experiment block.Experiments with the DSEE tools produced much lower surface roughness values than the 2-flute design over both workpiece materials. Feed rate had only minor effect on the surface roughness, with the lowest (iii)Burr formation was far lower for DSEE tools, while sharp slot edges were formed on CoCrMo workpiece material. 2-flute tools generally showed rougher and damaged side walls with large burr formation. Exp 5 again provided the most optimal machining conditions for this material. Burr formation was more significant on Ti6Al4V material for both tool types, however the DSEE tool showed far better results as feed per tooth increased, which indicated higher feed rates and spindle speeds were necessary for micro-milling with this material.(iv)Shorter and more discontinuous chips were formed during machining by the DSEE tools, while the 2-flute tools produced longer and more continuous chips. DSEE tools reduced the length of the chip by almost half in comparison to 2-flute tools, while only slightly increasing chip width.(v)All DSEE tools sustained very little tool wear throughout the experiments, with most tools still being considered “New”, according to both analysis methods of the developed tool wear criterion. Based on the experimental results, the conventional 2-flute tool design is not capable of micro-milling very hard materials and is prone to the major issues of massive tool wear, tool breakage and edge chipping when machining.A micro-milling DSEE tool design was proposed and achieved for machining of very hard and wear resistant materials with the purpose of significantly reducing tool wear and ensuring high quality machined slots. To fulfil such stringent requirements, the tool has met all the criteria proposed in the design specifications, namely, high stiffness, high strength, high durability, simplified and radially symmetric geometry and efficient chip evacuation properties.The main conclusions from this study can be drawn as follows.


Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo’s ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders. Typically, BM morphology assessment involves skilled technicians performing a differential count followed by verification and diagnosis by experienced hematopathologists. However, this process heavily relies on the expertise of technicians and pathologists and is time-consuming, which limits the overall efficiency of BM assessment9. Therefore, there is a pressing need for an automated approach to conducting standardized BM cell differential counts.The morphological examination of bone marrow (BM) nucleated cells plays a crucial role in the diagnosis of various hematological diseases, including acute leukemia (AL), chronic leukemia (CL), myelodysplastic syndrome (MDS), plasma cell myeloma (PCM), and hemorrhagic disease. It is considered one of the most critical diagnostic procedures, alongside immunological diagnosis and cytogenetics diagnosis according to the diagnostic guidelines of hematopoietic cancers issued by the World Health Organization (WHO)10. CNNs, being representative algorithms of deep learning, have gained widespread usage in computer-aided systems11. Their exceptional ability to extract image features was showcased when CNNs achieved top performance in the ImageNet 2012 competition. Since then, numerous studies have concentrated on CNN development and its application in clinical settings, to reduce the workload of medical professionals in object recognition and image classification and improve the precision of clinical diagnosis14. Automated pathology analysis systems developed based on CNN models are mainly used for histopathology and peripheral blood morphology, and there are fewer reports on the identification of BM nucleated cells until the last five years when they have been rapidly developed 17. By combining CNN and Gabor, Huang et al. created the innovative MGCNN framework for classifying blood cells. In comparison to conventional CNNs, this unique approach significantly increases classification accuracy but comes at a higher computational cost18. Liu et al. were able to analyze 200 fields in 16 min using a faster Region-Convolutional Neural Network (R-CNN) for BM imaging cell detection, taking an average of 4.8 s per image and achieving an accuracy of 0.496. However, the microscope's focus had to be manually adjusted throughout the observation to maintain a clear field of view19. Eckardt et al. used a multi-step deep learning methodology to separate cells from pictures of BM to discriminate between acute myeloid leukemia (AML) and healthy cells and to forecast the state of the Nucleophosmin 1 (NPM1) mutation, the most prevalent mutation in AML. However, this system requires the manual selection of areas for disease classification as judged by the pathologist, making the results potentially erroneous20. To detect acute lymphoblastic leukemia (ALL) in microscopic blood pictures, Atteia et al. are optimized using the Bayesian optimization technique. On a holdout test set, the best CNN model determined by the Bayesian optimization approach for ALL detection recorded 100% accuracy, specificity, and sensitivity21.A convolution neural network (CNN) is a kind of feedforward neural network that consists of convolution computation and depth structure23. We are committed to further improving the Morphogo system to enhance its performance and clinical value in assisting with the diagnosis of hematologic diseases.The Morphogo system we have developed overcomes many of these limitations, enabling efficient and accurate identification and classification of BM nucleated cells. According to our previous research, The Morphogo system integrates digital imaging of BM smear with artificial intelligence-based automatic BM cell differential count and has shown high accuracies in identifying various cell types, including granulocytic cells, erythroid cells, lymphoid cells, plasma cells, and monocytic cells, and even metastatic cancer cells8. The study was approved by the Ethics Committee of Guangzhou Kingland Medical Laboratory Center. The detailed information of the enrolled BM cases was listed in Table This was a retrospective study. 508 BM cases were collected from Kingmed Diagnostics from October 2021 to December 2021. Following the recommendations of pathologists, the BM smears were divided into five groups, denoted G1–G5, based on the extent of pathological and cell morphological changes. The diseases grouped within each category are as follows: G1: Relatively normal cases; G2: Disorders with quantitative abnormalities primarily affecting mature cells, including anemia, bleeding/thrombosis, myeloproliferative neoplasms (MPN), chronic myeloid leukemia (CML); G3: Disorder follow-up cases; G4: Malignant hematological disorders characterized by a substantial proliferation of blasts and immature cells, including acute leukemia (AL), Multiple myeloma (MM); G5: Disorders associated with a higher occurrence of abnormal cells, including megaloblastic anemia (MgA), myelodysplastic syndrome (MDS), Chronic lymphoproliferative disease (CLPD). All BM smears underwent appropriate staining using the Wright-Giemsa method, ensuring the quality aligned with the recommendation of the nation guide to clinical laboratory procedures  or the international council for Standardization in Hematology (ISH)Morphogo system is a CNN-based Artificial Intelligence (AI) system developed by Hangzhou Zhiwei Information and Technology Ltd that is used to perform a differential count of BM nucleated cells automatically.The workflow is as follows: 1) The Morphogo system initiates an automated scan of the BM smear using a 40 × objective lens, capturing a whole slide image (WSI) in the process. This enables the system to count megakaryocytes and identify the adaptive area for cell analysis. (2) Subsequently, the system switches to a 100 × objective lens to capture images of the designated area. Using CNN, the system identifies BM nucleated cells within this area and performs a differential cell count until a specified number of cells are obtained. (3) Before finalizing and releasing the cell morphology report, the data undergoes review by a pathologist. , negative predictive value (NPV), and accuracyIn the process of Morphogo scanning and analyzing BM smears, intelligent algorithms play an important role. There are several key algorithms involved in this process. The first is the slide scanning area algorithm, which extracts the slide area to be observed by mimicking the human task-based visual object attention mechanism to determine the 40 × scanning coverage. The second is the auto-focal plane algorithm. When the camera rapidly captures more than 100 images with varying sharpness at different object distances, the Sobel operator is applied to extract the gradient values in different directions of the images. By quantifying image clarity using a dedicated function, the algorithm identifies the clearest regions within each image and an image fusion algorithm is then employed to merge these regions, ensuring the best clarity for every nucleated cell within the field of view.Then, the 40 × full-slide assembling algorithm is utilized to introduce feature changes while maintaining a consistent scale, and the key points of the image are identified by the Gaussian differential function, and the key points are matched based on Ransac algorithm, achieving seamless assembly of the image and generating a WSI. Once the WSI is obtained, an area selection algorithm is used to select an optimal area for 100 × cell imaging. In 100 × cell images, a cell segmentation method based on saturation clustering is employed to accurately separate and locate the nucleated cells for differential count. Finally, the classification of BM nucleated cells is realized by a deep learning algorithm. This algorithm utilizes expert-labeled cell images and incorporates different types of cell morphological characteristics. By leveraging and the updated big data platform, which provides a continually expanding dataset, the algorithm achieves accurate classification and analysis of BM nucleated cells.The Morphogo system, which has been trained by more than 2.8 million BM nucleated cells, has now developed and refined to the point where it can automatically scan and analyze BM smears in less than 10 min while detecting more than 35 different types of nucleated cells  and analyzed by two-tailed Student’s t-test. p < 0.05 were considered statistically significant differences.Excel version 2016 was used to analyze the sensitivity, specificity, PPV, NPV, and accuracy of Morphogo’s cell classification by assuming pathologists’ annotations as the absolute true cell classification. The correlations of cell proportions were plotted by GraphPad Prism 7.0. Kappa and ICC for two different methods were performed by IBM SPSS Statistics 20 to evaluate the consistency. To interpret the correlation, the r-value is as follows: r less than 0.09 was no correlation; 0.1–0.3 was a weak correlation; 0.3–0.5 a was a moderate correlation. 0.5–1.0 was a high correlationAll of the above methods were performed by the relevant guidelines and regulations.This study was approved by the Ethics Committee of Guangzhou Kingmed Diagnostics Medical Laboratory Center. Because abandoned samples of routine clinical detections were collected and clinical case information was used, the Ethics Committee of Guangzhou Kingmed Diagnostics Medical Laboratory Center therefore has approved the application for performing the study with the exemption of informed consent from all participants.The high-resolution digital images of BM nucleated cells from the ROI were acquired using the Morphogo system. These cell images were categorized into 25 categories Fig. . Cell clTo evaluate the cell classification performance of the Morphogo system under different pathological conditions, the Morphogo system was applied to patient cases with more than 14 types of hematological diseases. The evaluation indicators were calculated for each disease condition and are shown in Table . Morphogo system showed positive correlation between pathologists and Morphogo system in the classification of myeloblast , except for promonocytes , and slightly lower for G4. Based on these results, the diagnosis results of the Morphogo system for most hematological diseases should be correct.To further verify the application value of the Morphogo system in the diagnosis of hematological diseases, the diagnoses made based on the Morphogo system were compared to the pathologists proofreading. The evaluation was made for each sample group G1–G5) in terms of intraclass correlation coefficient (ICC) and 95% CI. As shown in Table  in termsMorphogo system can complete automatic scanning continuously, and efficiently, with a success rate of 99.4%. The average time of a single slide scan is 7:46 (min), and most of the slide scanning time is concentrated in 5–9 min. The Morphogo system takes 7.46 ± 0.002 min/sheet to identify and count BM cells (Table 28 in recognizing and classifying diverse medical images. Our results, obtained using Morphogo, a cell morphology analysis system created using CNN models, indicate that rapid advancements in artificial intelligence will enable automated hematologic disease screening systems to realize their full potential.One of the most challenging steps in the workup of diagnosis of blood diseases is the morphological classification of BM nucleated cells, and the effectiveness of the classifier determines its utility in blood disorder diagnostics. CNN models, currently the leading classification framework, have shown superior performance compared to manual cellular morphological feature detection To enhance the CNN’s ability to discern potential relationships between BM nucleated cells during the learning process, we trained the CNN on the discriminative features of BM nucleated cells using 2.3 million BM cell images. We then tested the trained model on over 0.5 million cell images collected from various hospitals. This extensive database is beyond the reach of most models. The Morphogo system can now identify more than 35 classes of BM nucleated cells, including certain pathological cell types, and a few non-hematopoietic cells. Our results showed that the Morphogo system achieves high sensitivity, specificity, PPV, NPV, and accuracy in the classification and counting of 25 classes of BM nucleated cells. Moreover, the Morphogo system’s cell differential results were in substantial agreement with those of pathologists’ proofreading. Furthermore, the Morphogo system has the capability to automatically scan, identify and count BM nucleated cells, with an average processing time of 7.46 min. This indicates a substantial potential for the Morphogo system to enhance the efficiency of BM cell morphology analysis.25. For example, a promonocyte is often misidentified as a monocyte. Both manual counting and smart counting are affected by staining differences, and increasing the training data does not significantly improve accuracy29. The current Morphogo system cannot accurately distinguish subtle differences between morphologically similar cells. This limitation may explain why the sensitivity and PPV performance were not satisfactory in the identification of promonocytes. Furthermore, the image quality of BM nucleated cells depends on several factors such as the quality of BM smear preparation, the pathological condition, and the imaging process13. These factors can contribute to inaccuracies in BM cell identification. The morphology of blasts in AL of G4 is more uniform, while in MDS of G5, blasts tend to be polymorphic and malformed13. Consequently, blasts are easier to be identified and classified in AL, and difficult to be identified in MDS, which might be the cause of the higher misdiagnosis rate in some cases when using the Morphogo system compared to pathologists’ manual review. However, due to the large number of BM samples processed daily and the laborious and time-consuming nature of BM cell differential counting, some laboratories only count 100–200 cells in each BM smear. By utilizing the Morphogo system, they can review AI-based cell differential count results on a computer screen, dramatically improving the efficiency of laboratory work. The Morphogo system can analyze a larger number of cells in a shorter time, allowing pathologists to review more cells and avoid misdiagnosing critical morphological changes, ultimately reducing the misdiagnosis rate. Furthermore, the Morphogo system provides a standardized and digital approach to cell differential counting, enabling more reliable and repeatable assessment of morphology, and enhancing the overall quality control of BM morphology assessment. It also facilitates better comparison and communication among technicians and pathologists, ultimately leading to more effective patient care.The study provided pathologists with a potential application of AI in the morphology examination of BM smears. However, as previous research has reported, even experienced pathologists find it challenging to identify small differences between cells with similar morphological characteristics are difficult to identifyThis study employed a single-center method, where all BM smears were prepared in the same laboratory and digitally processed. The performance evaluation of the Morphogo system focused on identifying BM nucleated cells in common hemato-pathological conditions, and the dataset reasonably reflects the morphological changes of most cell types. However, this study still had some limitations. Firstly, it was limited to 14 common diseases, and the number of cases was insufficient to determine whether the Morphogo system’s AI performance would be consistent across all common hematopathological diseases and rare conditions. Secondly, efforts should be made to minimize the impact of staining variations on categorization strategies. Last but not least, we did not specifically collect samples of cells with dysplastic abnormalities during the initial development of the algorithm, serving as a training sample. As a result, when performing the statistical analysis, we discovered that the quantities of various types of qualitative cellular changes were not sufficient. In the future, further studies should be conducted using a larger number of BM samples that encompass a broader range of hematological diseases from multiple laboratories. This would help to further validate the BM cell identification performance of the Morphogo system more comprehensively.CNN-based Morphogo system could classify and count BM nucleated cells to assist pathologists to diagnose hematological diseases. The Morphogo system is a potential digital analysis system that provides a more objective and efficient method for BM morphology assessment.Supplementary Information.


Background/aim: Hypertensive nephropathy (HN) is a common complication of hypertension. Traditional Chinese medicine has long been used in the clinical treatment of Hypertensive nephropathy. However, botanical drug prescriptions have not been summarized. The purpose of this study is to develop a prescription for improving hypertensive nephropathy, explore the evidence related to clinical application of the prescription, and verify its molecular mechanism of action.Methods: In this study, based on the electronic medical record data on Hypertensive nephropathy, the core botanical drugs and patients’ symptoms were mined using the hierarchical network extraction and fast unfolding algorithm, and the protein interaction network between botanical drugs and Hypertensive nephropathy was established. The K-nearest neighbors (KNN) model was used to analyze the clinical and biological characteristics of botanical drug compounds to determine the effective compounds. Hierarchical clustering was used to screen for effective botanical drugs. The clinical efficacy of botanical drugs was verified by a retrospective cohort. Animal experiments were performed at the target and pathway levels to analyze the mechanism.Results: A total of 14 botanical drugs and five symptom communities were obtained from real-world clinical data. In total, 76 effective compounds were obtained using the K-nearest neighbors model, and seven botanical drugs were identified as Gao Shen Formula by hierarchical clustering. Compared with the classical model, the Area under the curve (AUC) value of the K-nearest neighbors model was the best; retrospective cohort verification showed that Gao Shen Formula reduced serum creatinine levels and Chronic kidney disease (CKD) stage . With respect to target and pathway enrichment, Gao Shen Formula acts on inflammatory factors such as TNF-α, IL-1β, and IL-6 and regulates the NF-κB signaling pathway and downstream glucose and lipid metabolic pathways.Conclusion: In the retrospective cohort, we observed that the clinical application of Gao Shen Formula alleviates the decrease in renal function in patients with hypertensive nephropathy. It is speculated that Gao Shen Formula acts by reducing inflammatory reactions, inhibiting renal damage caused by excessive activation of the renin-angiotensin-aldosterone system, and regulating energy metabolism. Hypertensive nephropathy (HN) is one of the common complications of hypertension and can significantly increase the prevalence of chronic kidney disease (CKD) . AccordiBotanical drugs have advantages in improving HN symptoms. In the theory of traditional Chinese medicine (TCM), the use of botanical drugs emphasizes the symptom characteristics of the population. According to existing research , compareBotanical drugs are rich in compounds that act on multiple targets of the disease and improve the imbalance of the disease biological networks . HN invoThis study was based on data derived from the EMRs of patients with HN. The hierarchical network extraction algorithm and the fast unfolding algorithm were used to summarize the network information on patients' prescriptions and disease symptoms. A machine learning model was used to comprehensively analyze the clinical information and biological characteristics and obtain the core prescription Gao Shen Formula (GSF). Spontaneously hypertensive rats were used to establish a model for the verification of the mechanism of action of GSF .Chinese Pharmacopoeia.A total of 30,695 electronic medical records of patients diagnosed with hypertension were collected from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 1 July 2014 to 31 May 2017. A total of 2,055 patients with HN were extracted. The diagnosis, demographic information (such as age and sex), chief complaint, medication prescriptions, and results of auxiliary examinations were collected from the patients’ EMRs. The patients’ symptoms were described using standard terminology, and the database was standardized with reference to the Medical Subject Headings (MeSH) and the In this study, prescriptions were extracted from the EMRs of HN patients. Setting botanical drugs as the node and the frequency of occurrence of two botanical drugs in different prescriptions as weights, a weighted prescription network was established. The hierarchical network extraction algorithm  was usedGephi (0.9.4) was used to implement the fast unfolding algorithm.The fast unfolding algorithm  has beenThe compounds and targets of core botanical drugs were collected from public databases, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) , SymMap HN-related genes were collected from the GEO database. The data were analyzed online using the GEO2R tool and supplemented using data from several mature databases, including Online Mendelian Inheritance in Man (OMIM) , DisGeNEHomo sapiens database to establish the protein‒protein interaction network (PPIN). In this study, shortest path analysis was used to identify the botanical drugs that are closely related to HN in the PPIN. The Dijkstra algorithm is a classical network node shortest-distance algorithm that is used to calculate the shortest distance between nodes in the PPIN. Nodes with a shortest distance of less than three were defined as strongly connected nodes  in R 3.6.3 was used to evaluate the compactness of compounds in each module in the matrix.To evaluate the regulation of each HN symptom by different compounds, this study summarized the number of genes regulated by different compounds in different symptom communities and established a frequency matrix. The Manhattan distance (Man) implemented in the Oral availability reflects the extent to which compounds are absorbed by the gastrointestinal tract and metabolized by the liver and is used as an important indicator of a compound’s effectiveness . The OB The Jaccard similarity coefficient (Jac) is used to measure the similarity between datasets. In this study, a Jac value was calculated for each compound target. The average Jac value was used to quantify the similarity of the botanical drug targets.BioConductor, implemented in the GOSemSim package (version 2.12.1) in R 3.6.3, was used to provide annotation data (The semantic similarity of Gene Ontology (GO) provides a method for calculating similarities among genes. In this study, GO biological process semantic similarity (GoSim) was used to measure the similarity between regulatory genes affected by compounds and biological processes related to HN. ion data .K-nearest neighbors (KNN) is a supervised machine learning algorithm that can generate a variety of feature information about compounds, match the input features with the expected output to create a learning function, and complete the classifier after cross-learning to adjust the parameters. The KNN model is sensitive to local information on interactions. KNN can be used to reflect the connection characteristics of input compounds and is more reliable for analysis of the targets of botanical drugs . In thispyrwr package (version 1.0.0) in Python 3.7.5.To determine the screening criteria to be used in the model, the random walk with restart (RWR) algorithm was used to find the key compounds. The HN-related gene was set in the PPIN as the seed node, and the restart probability was 0.75 . The lowkknn package (version 1.3.1) in R 3.6.3. To avoid data waste, the training uses 10 cross-validations to complete the model performance evaluation  were used as the input information for the KNN model and implemented in the aluation .To evaluate the performance of the KNN model, three other KNN control models were designed; they included input only OB (Comparison 2), input only Jac (Comparison 3), and input only gene regulatory information including GoSim and Man (Comparison 4).Receiver operating characteristic (ROC) curves of the four models were drawn, and the area under the curve (AUC) values were calculated to determine the effectiveness of the original model. After determining the best model, the point closest to the upper left corner of the ROC curve was used as the threshold point. Machine learning models such as support vector machines (SVMs), gradient boosting decision trees (GBDTs), and Bayesian networks (BNs), which are widely used in botanical drug bioinformation network analysis , were coThe KNN model can perform the screening of effective compounds. To effectively evaluate the effectiveness of botanical drugs, this study calculated the number of effective compounds contained in each botanical drug and used the hierarchical clustering algorithm (HCT) to classify and screen botanical drugs. In this study, Euclidean distance is used, and the clustering method of the org.Hs.e.g.db package (version 3.10.0) in R 3.6.3 was used to annotate the regulatory genes affected by GSF, the clusterProfiler package (version 3.14.3)   in R 3.6 3.14.3)  was usedMatching package (version 4.911) in R 3.6.3, was used to screen for matching patients, stabilize the tendency score between groups, and achieve a baseline balance among the patients.To evaluate the reliability of the KNN model and the HCT screening results, a retrospective cohort study was designed and used to evaluate the efficacy of GSF treatment based on real-world data. The retrospective cohort was established by analyzing the EMRs of 2,055 HN patients, screening both patients whose prescriptions included botanical drugs composed of GSF and patients who were not treated with botanical drugs, and evaluating changes in their clinical indices after six months of treatment. To ensure a balance among the selected patients, the tendency score was used to comprehensively quantify baseline factors such as age, sex, hospitalization time, and renal function, and the inverse probability processing weighted model was used. Inverse propensity of treatment weighting (IPTW) , implemeThe inclusion criteria were as follows: 1) a history of hypertension for more than five years; 2) persistent microalbuminuria or mild to moderate albuminuria during the course of the disease; 3) renal dysfunction caused by primary renal disease with hypertension and exposure to nephrotoxic substances, congenital or hereditary kidney disease, or other diseases; 4) a clear diagnosis in the medical record; and 5) the patient’s data in the medical record are complete.The exclusion criteria were as follows: 1) secondary hypertension; 2) severe cardiovascular or cerebrovascular disease, malignant tumors, severe hematopoietic system, respiratory system, digestive system, or infectious disease; 3) incomplete auxiliary examination of renal function during hospitalization; and 4) hospitalization time less than 1 week.The data of each eligible patient began with the electronic medical record recorded during hospitalization. The items collected include 1) the patient’s demographic characteristics ; 2) clinical features including length of hospital stay, serum creatinine, uric acid, urinary protein, blood lipids, and other parameters; and 3) details of the patient’s treatment, including the composition of any traditional Chinese medicine prescriptions used. The estimated glomerular filtration rate (eGFR) was estimated using the CKD-EPI formula . The CKDThe sample size was estimated based on reports in the clinical literature and on the previous research performed by the research group. The effective rate of modern medicine for symptomatic treatment is approximately 71% and that of combined traditional Chinese medicine is approximately 88%. The formula used to calculate the sample size is as follows:The traditional Chinese medicine prescriptions taken by patients in the GSF group included the constituent drugs of GSF. During the 6-month study period, all patients had records of traditional Chinese medicine prescriptions during outpatient visits or hospitalization in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine and blood or urine test results related to their treatment with traditional Chinese medicine. During this period, all patients took antihypertensive drugs and patients with a GFR greater than 60 Thirty 14-week-old male spontaneously hypertensive (SHR) rats with body weights of 280–300 g were purchased from SPF (Beijing) Biotechnology Co., Ltd., China . Ten age-matched Wistar Kyoto (WKY) rats with body weights of 280–300 g were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd., China . Chinese granule herbal extracts of GSF were purchased from Tiangjiang Pharmaceutical Co., Ltd, China. Irbesartan tablets [lot No. 0000009987] were purchased from Zhejiang Huahai Pharmaceutical Co., Ltd. China. The SHR and WKY rats were housed in standard plastic cages with a 12-h light/dark cycle at 23°C ± 1 °C and given free access to food and water. After a 10-week acclimation period, the 24-week-old SHR rats were randomly divided into three groups: 1) a GSF group in which each animal was treated with Chinese granule herbal extract at 4.5 g/kg body weight per day (i.g.); 2) a positive group in which each animal received irbesartan tablets 13.5 mg/kg body weight per day (i.g.); and 3) an SHR group in which each animal received 2 mL of saline per day (i.g.). The WKY group consisted of 10 rats, each of which received 2 mL of saline per day (i.g.). The intervention lasted for 8 weeks. The study was approved by the Animal Ethics Committee of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine and the Institutional Animal Care and Use Committee (IACUC) of Shandong University of Traditional Chinese Medicine.To evaluate histological changes in the kidney, the animals were euthanized, and their kidneys were removed and quickly immersed in 4% paraformaldehyde for 24 h. The kidney tissue was dehydrated, embedded in paraffin, and sectioned at a thickness of 3 μm. Finally, hematoxylin-eosin staining was performed, and the pathological changes in the animals’ kidney tissue were observed under a light microscope at 400-fold magnification.The kidney tissue was ground in liquid nitrogen and cleaved with a strong RIPA buffer . The protein concentration was determined using a bicinchoninic acid (BCA) protein quantitation kit . Primary antibodies targeting NF-κB p65 , NF-κB p65 (phospho-S536) , and beta-actin  were incubated overnight with the target protein at 4 °C. The samples were then incubated with HRP-conjugated secondary antibodies . Protein expression was measured using an enhanced chemiluminescence reagent (ECL) kit .p < 0.05 were retained as highly specific botanical drugs, they were Dianthus superbus L.  (qumai) 3–10 g/d, Plantago asiatica L.  (cheqianzi) 10–20 g/d, Curcuma phaeocaulis Valeton  (ezhu) 10–20 g/d, Euryale ferox Salisb.  (qianshi) 10–20 g/d, and Achyranthes bidentata Blume  (niuxi) 15–30 g/d. The botanical drugs in the EMR data were boiled for 30 min and filtered. The combination of two parts with a total of 14 botanical drugs produced 961 compounds and 1,328 regulatory targets from public databases.Information on 2,055 patients with HN was collected; the information included 1,499 prescriptions, including 425 botanical drugs that were used 35,734 times. The hierarchical network extraction algorithm identified the core botanical drugs, they included Astragalus mongholicus Bunge  (huanqi) 15–25 g/d, Salvia miltiorrhiza Bunge  (danshen) 10–20 g/d, Codonopsis pilosula (Franch.) Nannf.  (dangshen) 10–30 g/d, Poria cocos  (fuling) 15–30 g/d, Atractylodes macrocephala Koidz.  (baizhu) 10–20 g/d, Angelica sinensis (Oliv.) Diels  (danggui) 15–30 g/d, Citrus × aurantium f. deliciosa Pericarpium  (chenpi) 10–20 g/d, Pinellia ternata (Thunb.) Makino  (banxia) 3–6 g/d, and Glycyrrhiza uralensis Fisch.  (gancao) 3–10 g/d. Five traditional Chinese medicines with the highest RR values and A total of 244 symptoms were extracted from EMRs. The fast unfolding algorithm was used to divide the symptoms into 14 network communities, as shown in As shown in The biometric information on the quantized compound was taken as the input information. The ROC curve of HN and its symptom community  shows thp < 0.05). This combination of botanical drugs was used as an effective prescription, Gao Shen Formula (GSF), for HN.According to the statistics on the optimal number of compounds in each module of 14 core traditional Chinese medicines, a distance matrix was established, and HCT was used to classify the combinations of botanical drugs . Salvia SVM = 0.845, AUCGBDT = 0.868, and AUCBN = 0.530). This shows that the training setting of the KNN model was reasonable and had a stable prediction performance.Based on the ROC curves obtained for each model , the KNNGSF contains 57 core targets. To show its biological characteristics, a 7 × 57 matrix was established for the heatmap . Among tGSF was enriched in 46 KEGG pathways and 24 GO terms. Combined with the enrichment results for each symptom community, 13 × 46 and 13 × 24 matrices were established based on the number of enriched genes. The enrichment pathways are presented as heatmaps . The KEGAmong the enriched KEGG pathways, Salvia miltiorrhiza Bunge, Codonopsis pilosula (Franch.) Nannf., Pinellia ternata (Thunb.) Makino, and Glycyrrhiza uralensis Fisch. were significantly enriched in inflammation-related pathways such as the NF-κB signaling pathway, the HIF-1 signaling pathway, the TNF signaling pathway, and the NOD-like receptor signaling pathway and can regulate vascular endothelial proliferation pathways such as the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the VEGF signaling pathway. In addition, Salvia miltiorrhiza Bunge can regulate the cGMP-PKG signaling pathway, act on vascular smooth muscle, and regulate blood pressure. At the same time, Salvia miltiorrhiza Bunge can regulate the renin secretion pathway and the PI3K-Akt signaling pathway, regulate downstream inflammatory factors after renin activation, inhibit renal interstitial fibrosis, and antagonize the renin-angiotensin-aldosterone system. Glycyrrhiza uralensis Fisch. not only inhibits inflammation but also regulates the endocrine system, regulating protein, fatty acid, and glycogen metabolism by acting on the AMPK signaling pathway. Codonopsis pilosula (Franch.) Nannf. can also regulate fat and carbohydrate metabolism by affecting the PPAR signaling pathway. Angelica sinensis (Oliv.) Diels can regulate the cAMP pathway and affect the cytoskeleton. Euryale ferox Salisb. and Atractylodes macrocephala Koidz. can also act on cholinergic synapses and on the calcium signaling pathway, affect smooth muscle, and regulate blood pressure.Based on the obtained GO terms, GSF mainly regulates cytokines, chemokines, and growth factors. Salvia miltiorrhiza Bunge, Glycyrrhiza uralensis Fisch. and Pinellia ternata (Thunb.) Makino showed the most extensive regulation of cellular processes, including the regulation of DNA transcription factors and the production of a variety of amino acids.The treatment plans of a total of 286 patients with HN in the EMRs included GSF, and 279 individuals did not receive TCM treatment. Based on our calculation of sample size, 91 patients were needed in each group. To ensure that the baseline levels of the patients in the groups were consistent, the patients in each group were excluded according to IPTW,  and 91 pp > 0.05). After 6 months of treatment, the creatinine clearance rate in both groups increased (p < 0.05), the serum creatinine level in the GSF group was lower than that in the control group (p < 0.05), and more patients in the GSF group than in the control group experienced a decrease in the CKD stage . Based on this retrospective cohort validation, the addition of GSF to conventional Western medical treatment improves the renal function of patients and reduces their serum creatinine levels, and there are no evident adverse reactions.There was no significant difference in age, sex, length of stay, liver function, glucose metabolism, lipid metabolism, or renal function among the enrolled patients , Angelica sinensis (Oliv.) Diels  (9 g), Astragalus mongholicus Bunge  (9 g), Codonopsis pilosula (Franch.) Nannf.  (15 g), Pinellia ternata (Thunb.) Makino  (9 g), Curcuma phaeocaulis Valeton  (9 g), and Glycyrrhiza uralensis Fisch.  (6 g). The botanical drugs were boiled in water for 1 h, filtered, concentrated, dried, and pulverized to make granules. Granules of botanical drugs were purchased from Tiangjiang Pharmaceutical Co., Ltd, China.p < 0.05) and were lower than those in the model group (p < 0.05). There was no difference in blood pressure between the GSF group and the irbesartan group after treatment (p > 0.05). It is suggested that both GSF and irbesartan have hypotensive effects on SHR.After 8 weeks of drug treatment of SHR rats, systolic blood pressure and diastolic blood pressure decreased in the GSF group and in the irbesartan group (p < 0.05). The left ventricular ejection fraction of the rats in all four groups was in the normal range, and there was no significant difference among the groups (p > 0.05). It is suggested that GSF can antagonize ventricular hypertrophy caused by hypertension and that its effect is similar to that of irbesartan. As shown in p < 0.05), and there were no significant differences in the serum TNF-α levels of the high kidney formula group, the irbesartan group and the control group (p > 0.05). As shown in The interventricular septum at the end of diastole, the left ventricular posterior wall, and the left ventricular posterior wall at the end of systole were thinner in the GSF group and the irbesartan group than in the model group  increases, causing upregulation of the production of inflammatory cytokines such as TNF-α, IL-6, and IL-8 expression in kidney tissue. The mRNA and protein levels of type I and IV collagen are increased , and excAccording to the pathway enrichment results, the inflammatory pathway related to hypertensive renal damage that is regulated by GSF is mainly the NF-κB signaling pathway. GSF may regulate TNF-α, IL-1β, IL-6, and other inflammatory factors indirectly or directly, thereby inhibiting the NF-κB signaling pathway and further inhibiting the inflammatory response caused by inflammatory cytokines, the upregulation of the MAPK signaling pathway caused by MAPK8 and TNF, and the interstitial fibrosis caused by the increase in serine . GSF treIn the early stage of hypertensive renal damage, glomerular metabolism increases, especially GTP metabolism, lipid breakdown, and the production of various amino acids. Moreover, due to the increase in lipid oxidation, an inflammatory reaction can be induced , and thiThe drugs contained in GSF can regulate insulin resistance and the PPAR signaling pathway; improve the utilization of lipids and sugars by the body through specific effects on INS, PPARG, ADIPOQ, and other targets ; and improve the efficiency of utilization of sugars and lipids . GSF canTo verify the anti-inflammatory mechanism of GSF, this study used SHR rats to establish a disease model. After eight weeks of intervention with GSF, it was determined that GSF reduced renal NF-κB p65 and phosphorylated p65 levels, indicating that NF-κB dimer decreased, signal conduction was weakened, and NF-κB activation was inhibited, thereby reducing the production of inflammatory factors similar to serum TNF-α. Fewer inflammatory cells were present in the pathological sections obtained from the kidneys of the animals in the GSF group, and structural changes in the nephrons were not notable, indicating that GSF alleviates renal inflammation. The animals in the GSF group also had decreased blood pressure after intervention. The Color Doppler ultrasound showed that the left ventricular myocardium was thinner in the animals in the GSF group than in those in the model group, proving that it delays myocardial remodeling. No adverse reactions or death were observed during the experiment, which could preliminarily indicate its safety.This research innovatively combines network pharmacology with real-world data mining and uses the KNN model to process information on diseases and botanical drugs, closely linking systems biology with clinical data. To determine the efficacy of the model, it was compared with other classical machine learning models, and its efficacy was verified in a retrospective cohort. Finally, animal experiments were used to verify the core mechanism of action of GSF. The results show that compared with traditional models, the KNN model can better handle the heterogeneity and complexity of the relationship between genome data and TCM clinical information. GSF obtained using this method was shown to have a practical effect after verification, and the findings have a certain reference significance for the analysis of target organ damage after TCM treatment of hypertension.At the same time, this study also has limitations. First, although the symptoms of the patients are included in the evaluation system, this study still uses the patients as a whole dataset and fails to make full use of the complex clinical individualized differences reflected by the real-world data. Second, the sample size used for model training in this study was limited. Although we used a variety of models for comparative verification, further research is needed to improve the model’s performance. Third, the animal experiments conducted in this study only verified the most fundamental anti-inflammatory mechanism, and no experimental analysis of the changes in downstream pathways of the inflammatory response was performed; thus, further research on the pathways and mechanisms involved is needed. In terms of clinical effects, this study features a small sample size, multiple risks of bias, heterogeneity of GSF among patients, and limited measurement of results. In addition, randomized trials should be conducted to determine the efficacy and safety of GSF in the treatment of HN , 2.This study was based on EMR data for patients with HN collected at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2014 to 2017. It identified core botanical drugs for the treatment of HN. After establishment of the PPIN, machine learning model training was used to obtain an effective compound screening model based on information on the biological characteristics of compounds, and a hierarchical clustering screening of botanical drugs was conducted based on the model results, retaining the important botanical drugs as GSF for treating HN. From the perspective of targets and pathways, it was found that GSF plays a multilevel biological regulatory role in which it controls inflammatory reactions that lead to hypertensive kidney damage and reduces the damage caused by excessive activation of the RAAS system.